Connect the dots: sketching out microbiome interactions through networking approaches

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment

Bayesian nonparametric models for spatially indexed data of mixed type

We develop Bayesian nonparametric models for spatially indexed data of mixed type. Our work is motivated by challenges that occur in environmental epidemiology, where the usual presence of several confounding variables that exhibit complex interactions and high correlations makes it difficult to estimate and understand the effects of risk factors on health outcomes of interest. The modeling approach we adopt assumes that responses and confounding variables are manifestations of continuous latent variables, and uses multivariate Gaussians to jointly model these. Responses and confounding variables are not treated equally as relevant parameters of the distributions of the responses only are modeled in terms of explanatory variables or risk factors. Spatial dependence is introduced by allowing the weights of the nonparametric process priors to be location specific, obtained as probit transformations of Gaussian Markov random fields. Confounding variables and spatial configuration have a similar role in the model, in that they only influence, along with the responses, the allocation probabilities of the areas into the mixture components, thereby allowing for flexible adjustment of the effects of observed confounders, while allowing for the possibility of residual spatial structure, possibly occurring due to unmeasured or undiscovered spatially varying factors. Aspects of the model are illustrated in simulation studies and an application to a real data set

Glosarium Matematika

273 p.; 24 cm

Debiased-CAM for bias-agnostic faithful visual explanations of deep convolutional networks

Class activation maps (CAMs) explain convolutional neural network predictions by identifying salient pixels, but they become misaligned and misleading when explaining predictions on images under bias, such as images blurred accidentally or deliberately for privacy protection, or images with improper white balance. Despite model fine-tuning to improve prediction performance on these biased images, we demonstrate that CAM explanations become more deviated and unfaithful with increased image bias. We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for CAM and bias level predictions. With CAM as a prediction task, explanations are made tunable by retraining the main model layers and made faithful by self-supervised learning from CAMs of unbiased images. The model provides representative, bias-agnostic CAM explanations about the predictions on biased images as if generated from their unbiased form. In four simulation studies with different biases and prediction tasks, Debiased-CAM improved both CAM faithfulness and task performance. We further conducted two controlled user studies to validate its truthfulness and helpfulness, respectively. Quantitative and qualitative analyses of participant responses confirmed Debiased-CAM as more truthful and helpful. Debiased-CAM thus provides a basis to generate more faithful and relevant explanations for a wide range of real-world applications with various sources of bias

Variational Bayesian inference for comparison Var(1) models

Suppose that we wish to determine which models in a candidate set are most likely to have given rise to a set of observed data. Then, it is well-established that, from a Bayesian viewpoint, evaluation of the marginal likelihood for each candidate is a crucial step to this end. For the purposes of model comparison, this will enable subsequent computation of both Bayes’ factors and posterior model probabilities. Given its evident significance in this area, it is thus regrettable that analytic calculation of the marginal likelihood is often not possible. To tackle this problem, one recent addition to the literature is the variational Bayesian approach. In this thesis, it is seen that variational Bayes provides efficient, accurate approximations to both the marginal likelihood and the parameter posterior distribution, conditioned on each model. In particular, the theory is applied to ranking sparse, vector autoregressive graphical models of order 1 in both the zero and non-zero mean case. That is, our primary aim is to estimate the unknown sparsity structure of the autoregressive matrix in the process. Moreover, approximate, marginal posterior information about the coefficients of this matrix is also of interest. To enable rapid exploration of higher-dimensional graphical spaces, a Metropolis-Hastings algorithm is presented so that a random walk can be made between neighbouring graphs. The scheme is then tested on both simulated and real datasets of varying dimension.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research CouncilGBUnited Kingdo

Variational Bayesian inference for comparison Var(1) models

Suppose that we wish to determine which models in a candidate set are most likely to have given rise to a set of observed data. Then, it is well-established that, from a Bayesian viewpoint, evaluation of the marginal likelihood for each candidate is a crucial step to this end. For the purposes of model comparison, this will enable subsequent computation of both Bayes’ factors and posterior model probabilities. Given its evident significance in this area, it is thus regrettable that analytic calculation of the marginal likelihood is often not possible. To tackle this problem, one recent addition to the literature is the variational Bayesian approach. In this thesis, it is seen that variational Bayes provides efficient, accurate approximations to both the marginal likelihood and the parameter posterior distribution, conditioned on each model. In particular, the theory is applied to ranking sparse, vector autoregressive graphical models of order 1 in both the zero and non-zero mean case. That is, our primary aim is to estimate the unknown sparsity structure of the autoregressive matrix in the process. Moreover, approximate, marginal posterior information about the coefficients of this matrix is also of interest. To enable rapid exploration of higher-dimensional graphical spaces, a Metropolis-Hastings algorithm is presented so that a random walk can be made between neighbouring graphs. The scheme is then tested on both simulated and real datasets of varying dimension.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research CouncilGBUnited Kingdo

NASA/ASEE Summer Faculty Fellowship Program, 1990, Volume 1

The 1990 Johnson Space Center (JSC) NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston-University Park and JSC. A compilation of the final reports on the research projects are presented. The topics covered include: the Space Station; the Space Shuttle; exobiology; cell biology; culture techniques; control systems design; laser induced fluorescence; spacecraft reliability analysis; reduced gravity; biotechnology; microgravity applications; regenerative life support systems; imaging techniques; cardiovascular system; physiological effects; extravehicular mobility units; mathematical models; bioreactors; computerized simulation; microgravity simulation; and dynamic structural analysis

PHYSTAT-LHC Workshop on Statistical Issues for LHC Physics

A PHYSTAT workshop on the topic of Statistical issues for LHC physics was held at CERN. The workshop focused on issues related to discovery that we hope will be relevant to the LHC. These proceedings contain written versions of nearly all the talks, several of which were given by professional statisticians. The talks varied from general overviews, to those describing searches for specific particles. The treatment of background uncertainties figured prominently. Many of the talks describing search strategies for new effects should be of interest not only to particle physicists but also to scientists in other fields