Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

Cryogenic thermal control technology summaries

A summarization and categorization is presented of the pertinent literature associated with cryogenic thermal control technology having potential application to in-orbit fluid transfer systems and/or associated space storage. Initially, a literature search was conducted to obtain pertinent documents for review. Reports determined to be of primary significance were summarized in detail. Each summary, where applicable, consists of; (1) report identification, (2) objective(s) of the work, (3) description of pertinent work performed, (4)major results, and (5) comments of the reviewer (GD/C). Specific areas covered are; (1) multilayer insulation of storage tanks with and without vacuum jacketing, (2) other insulation such as foams, shadow shields, microspheres, honeycomb, vent cooling and composites, (3) vacuum jacketed and composite fluid lines, and (4) low conductive tank supports and insulation penetrations. Reports which were reviewed and not summarized, along with reasons for not summarizing, are also listed

Eyes on the mind : investigating the influence of gaze dynamics on the perception of others in real-time social interaction

ACKNOWLEDGMENTS This study was partially supported by a grant of the Köln Fortune Program of the Medical Faculty at the University of Cologne to Leonhard Schilbach and by a grant “Other Minds” of the German Ministry of Research and Education to Kai Vogeley. The authors would like to thank Stephanie Alexius and Leonhard Engels for their assistance in data collection.Peer reviewedPublisher PD

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted

Bayesian modelling of space-time interactions on the Lexis diagram

We propose a full model-based framework for a statistical analysis of incidence or mortality count data stratified by age, period and space, with specific inclusion of additional cohort effects. The setup will be fully Bayesian based on a series of Gaussian Markov random field priors for each of the components. Additional space-time interactions will be either modelled as space-period or space-cohort effects. Statistical inference is based on efficient algorithms to block update Gaussian Markov random fields, which have recently been proposed in the literature. We illustrate our approach in an analysis of stomach cancer data in West Germany

Why we interact : on the functional role of the striatum in the subjective experience of social interaction

Acknowledgments We thank Neil Macrae and Axel Cleeremans for comments on earlier versions of this manuscript. Furthermore, we are grateful to Dorothé Krug and Barbara Elghahwagi for their assistance in data acquisition. This study was supported by a grant of the Köln Fortune Program of the Medical Faculty at the University of Cologne to L.S. and by a grant “Other Minds” of the German Ministry of Research and Education to K.V.Peer reviewedPreprin

Medizinische und gesundheitsökonomische Bewertung der Radiochirurgie zur Behandlung von Hirnmetastasen

Background: Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets. Findings: Using our new analysis tool, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips® can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi. Conclusions: MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants

Evolution of ferroelectric domains in methylammonium lead iodide and correlation with the performance of perovskite solar cells

While more and more experimental evidence for the ferroelectricity of methylammonium lead iodide (MAPbI(3)) is being reported in the literature, the scientific community still controversially discusses whether or not the ferroelectric polarization has any influence on the performance of perovskite solar cells. In this work, the evolution of ferroelectric domains and their polarization orientation in MAPbI(3) thin films during thermal annealing are investigated using piezoresponse force microscopy (PFM) and Kelvin probe force microscopy (KPFM). Right after deposition and annealing for 5 s, small grains with non-uniform crystal orientation and polarization are formed. During the next 30-60 s, these small grains increase in diameter and large ferroelectric domains with out-of-plane polarization appear. In the annealing regime of several minutes to one hour, these large grains produce uniform domains with alternating in-plane polarization and (110) texture. The corresponding MAPbI(3) solar cells show a distinct performance enhancement and improved operational stability if the ferroelectric polarization is oriented in-plane. In contrast, solar cells with out-of-plane-polarized MAPbI(3) exhibit only moderate fill factors and reduced open-circuit voltages

A Bayesian Model for Spatial Disease Prevalence Data

The analysis of the geographical distribution of disease on the scale of geographic areas such as administrative boundaries plays an important role in veterinary epidemiology. Prevalence estimates of wildlife population surveys are often based on regional count data generated by sampling animals shot by hunters. The observed disease rate per spatial unit is not a useful estimate of the underlying disease prevalence due to different sample sizes and spatial dependencies between neighbouring areas. Therefore, it is necessary to account for extra-sample variation and and spatial correlation in the data to produce more accurate maps of disease incidence. For this purpose a hierarchical Bayesian model in which structured and un-structured overdispersion is modelled explicitly in terms of spatial and non-spatial components was implemented by Markov Chain Monte Carlo methods. The model was empirically compared with the results of the non-spatial beta-binomial model using surveillance data of Pseudorabies virus infections of wildboars in the Federal State of Brandenburg, Germany