Compositionally aware estimation of cross-correlations for microbiome data.

In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables.

Nitrogen and Nod factor signaling determine Lotus japonicus root exudate composition and bacterial assembly.

Symbiosis with soil-dwelling bacteria that fix atmospheric nitrogen allows legume plants to grow in nitrogen-depleted soil. Symbiosis impacts the assembly of root microbiota, but it is unknown how the interaction between the legume host and rhizobia impacts the remaining microbiota and whether it depends on nitrogen nutrition. Here, we use plant and bacterial mutants to address the role of Nod factor signaling on Lotus japonicus root microbiota assembly.

Predicting Tissue Loads in Running from Inertial Measurement Units.

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries.

Methods: We investigate a combined physics-based simulation and data-based method.

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory.

TOWARD BAYESIAN INFERENCE OF THE SPATIAL DISTRIBUTION OF PROTEINS FROM THREE-CUBE FÖRSTER RESONANCE ENERGY TRANSFER DATA.

Förster resonance energy transfer (FRET) is a quantum-physical phenomenon where energy may be transferred from one molecule to a neighbor molecule if the molecules are close enough. Using fluorophore molecule marking of proteins in a cell, it is possible to measure in microscopic images to what extent FRET takes place between the fluorophores. This provides indirect information of the spatial distribution of the proteins.

Second-order quasi-likelihood for spatial point processes.

Applications of spatial point processes for large and complex data sets with inhomogeneities as encountered, example, in tropical rain forest ecology call for estimation methods that are both statistically and computationally efficient. We propose a novel second-order quasi-likelihood procedure to estimate the parameters for a second-order intensity reweighted stationary spatial point process. Our approach is to derive first- and second-order estimating functions and then combine them linearly using appropriate weight functions.

Analysis of shape and spatial interaction of synaptic vesicles using data from focused ion beam scanning electron microscopy (FIB-SEM).

The spatial interactions of synaptic vesicles in synapses were assessed after a detailed characterization of size, shape, and orientation of the synaptic vesicles. We hypothesized that shape and orientation of the synaptic vesicles are influenced by their movement toward the active zone causing deviations from spherical shape and systematic trends in their orientation. We studied three-dimensional representations of synapses obtained by manual annotation of focused ion beam scanning electron microscopy (FIB-SEM) images of male mouse brain.

Multivariate product-shot-noise Cox point process models.

We introduce a new multivariate product-shot-noise Cox process which is useful for modeling multi-species spatial point patterns with clustering intra-specific interactions and neutral, negative, or positive inter-specific interactions. The auto- and cross-pair correlation functions of the process can be obtained in closed analytical forms and approximate simulation of the process is straightforward. We use the proposed process to model interactions within and among five tree species in the Barro Colorado Island plot.

Quasi-likelihood for Spatial Point Processes.

Fitting regression models for intensity functions of spatial point processes is of great interest in ecological and epidemiological studies of association between spatially referenced events and geographical or environmental covariates. When Cox or cluster process models are used to accommodate clustering not accounted for by the available covariates, likelihood based inference becomes computationally cumbersome due to the complicated nature of the likelihood function and the associated score function. It is therefore of interest to consider alternative more easily computable estimating functions.

Misclassification of iodine intake level from morning spot urine samples with high iodine excretion among Inuit and non-Inuit in Greenland.

Iodine nutrition is commonly assessed from iodine excretion in urine. A 24 h urine sample is ideal, but it is cumbersome and inconvenient. Hence, spot urine samples with creatinine to adjust for differences in void volume are widely used.

Disease risk estimation by combining case-control data with aggregated information on the population at risk.

We propose a novel statistical framework by supplementing case-control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case-control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation. The proposed method is computationally simple and more efficient than standard approaches based on case-control data alone.

A new estimation approach for combining epidemiological data from multiple sources.

We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently in order to make full use of the information contained in data.

Quantifying effects of habitat heterogeneity and other clustering processes on spatial distributions of tree species.

Spatially explicit consideration of species distribution can significantly add to our understanding of species coexistence. In this paper, we evaluated the relative importance of habitat heterogeneity and other clustering processes (e.g.

Interrater reliability of diagnostic methods in traditional Indian ayurvedic medicine.

This study assesses the interrater reliability of Ayurvedic pulse (nadi), tongue (jivha), and body constitution (prakriti) assessments. Fifteen registered Ayurvedic doctors with 3-15 years of experience independently examined twenty healthy subjects. Subjects completed self-assessment questionnaires and software analyses for prakriti assessment.

Intrarater and interrater reliability of pulse examination in traditional Indian Ayurvedic medicine.

Background: In Ayurveda, pulse examination () is an important tool to assess the status of three : , , and . Long historical use has been seen as a documentation of its efficacy; however, there is a lack of a quantitative measure of the reliability of the pulse examination method. The objective of this study was to test the intrarater and interrater reliability of pulse examination in Ayurveda.

Reliability studies of diagnostic methods in Indian traditional Ayurveda medicine: An overview.

Recently, a need to develop supportive new scientific evidence for contemporary Ayurveda has emerged. One of the research objectives is an assessment of the reliability of diagnoses and treatment. Reliability is a quantitative measure of consistency.

Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor.

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers.

Decomposition of Variance for Spatial Cox Processes.

Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions.

Repeatability of Pulse Diagnosis and Body Constitution Diagnosis in Traditional Indian Ayurveda Medicine.

In Ayurveda, pulse diagnosis and body constitution diagnosis have a long historical use; still, there is lack of quantitative measure of the reliability of these diagnostic methods. Reliability means consistency of information. Consistent diagnosis leads to consistent treatment and is important for clinical practice, education, and research.

Selection for environmental variation: a statistical analysis and power calculations to detect response.

Data from uterine capacity in rabbits (litter size) were analyzed to determine whether the environmental variance was partly genetically determined. The fit of a classical homogeneous variance mixed linear (HOM) model and that of a genetically structured heterogeneous variance mixed linear (HET) model were compared. Various methods to assess the quality of fit favor the HET model.

A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics.

In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations.

An estimating function approach to inference for inhomogeneous Neyman-Scott processes.

This article is concerned with inference for a certain class of inhomogeneous Neyman-Scott point processes depending on spatial covariates. Regression parameter estimates obtained from a simple estimating function are shown to be asymptotically normal when the "mother" intensity for the Neyman-Scott process tends to infinity. Clustering parameter estimates are obtained using minimum contrast estimation based on the K-function.

Evidence for genetic control of adult weight plasticity in the snail Helix aspersa.

Phenotypic plasticity and canalization are important topics in quantitative genetics and evolution. Both concepts are related to environmental sensitivity. The latter can be modeled using a model with genetically structured environmental variance.

Normal linear models with genetically structured residual variance heterogeneity: a case study.

Normal mixed models with different levels of heterogeneity in the residual variance are fitted to pig litter size data. Exploratory analysis and model assessment is based on examination of various posterior predictive distributions. Comparisons based on Bayes factors and related criteria favour models with a genetically structured residual variance heterogeneity.

Bayesian prediction of spatial count data using generalized linear mixed models.

Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction.