Functional Mixed Membership Models

Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this paper, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Lo\`eve theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance structure. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions.Comment: 77 pages, 16 figure

A Covariance Based Clustering for Tensor Objects

Clustering of tensors with limited sample size has become prevalent in a variety of application areas. Existing Bayesian model based clustering of tensors yields less accurate clusters when the tensor dimensions are sufficiently large, sample size is low and clusters of tensors mainly reveal difference in their variability. This article develops a clustering technique for high dimensional tensors with limited sample size when the clusters show difference in their covariances, rather than in their means. The proposed approach constructs several matrices from a tensor, referred to as transformed features, to adequately estimate its variability along different modes and implements a model-based approximate Bayesian clustering algorithm with the matrices thus constructed, in place with the original tensor data. Although some information in the data is discarded, we gain substantial computational efficiency and accuracy in clustering. Simulation study assesses the proposed approach along with its competitors in terms of estimating the number of clusters, identification of the modal cluster membership along with the probability of mis-classification in clustering (a measure of uncertainty in clustering). The proposed methodology provides novel insights into potential clinical subgroups for children with autism spectrum disorder based on resting-state electroencephalography activity.National Science Foundation Grant DMS-2220840, DMS-2210672 and Office of Naval Research Grant N00014-18-1-274

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models

Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is proposed with a focus on scalability and interpretability. The novel probabilistic representation of mixed membership is based on convex combinations of dependent multivariate Gaussian random vectors. In this setting, scalability is ensured through approximations of a tensor covariance structure through multivariate eigen-approximations with adaptive regularization imposed through shrinkage priors. Conditional weak posterior consistency is established on an unconstrained model, allowing for a simple posterior sampling scheme while keeping many of the desired theoretical properties of our model. The model is motivated by two biomedical case studies: a case study on functional brain imaging of children with autism spectrum disorder (ASD) and a case study on gene expression data from breast cancer tissue. These applications highlight how the typical assumption made in cluster analysis, that each observation comes from one homogeneous subgroup, may often be restrictive in several applications, leading to unnatural interpretations of data features.Comment: arXiv admin note: text overlap with arXiv:2206.1208

The Grizzly, March 31, 2016

Investment Club Back in Business • Politics Class Goes to NY for Model UN • UCSG to Hold Executive Elections • Q&A with President and VP Candidates • New Platform for Student Expression • Leaving Behind a Legacy • Opinions: This Election is About You ; Advocacy on Issues You Care About • Acing the Competition • Neff Strives in Nethttps://digitalcommons.ursinus.edu/grizzlynews/1687/thumbnail.jp

Constant Transmission Properties of Variant Creutzfeldt-Jakob Disease in 5 Countries

Variant Creutzfeldt-Jakob disease (vCJD) has been reported in 12 countries. We hypothesized that a common strain of agent is responsible for all vCJD cases, regardless of geographic origin. To test this hypothesis, we inoculated strain-typing panels of wild-type mice with brain material from human vCJD case-patients from France, the Netherlands, Italy, and the United States. Mice were assessed for clinical disease, neuropathologic changes, and glycoform profile; results were compared with those for 2 reference vCJD cases from the United Kingdom. Transmission to mice occurred from each sample tested, and data were similar between non-UK and UK cases, with the exception of the ranking of mean clinical incubation times of mouse lines. These findings support the hypothesis that a single strain of infectious agent is responsible for all vCJD infections. However, differences in incubation times require further subpassage in mice to establish any true differences in strain properties between cases

Similarities of Variant Creutzfeldt-Jakob Disease Strain in Mother and Son in Spain to UK Reference Case

We investigated transmission characteristics of variant Creutzfeldt-Jakob disease in a mother and son from Spain. Despite differences in patient age and disease manifestations, we found the same strain properties in these patients as in UK vCJD cases. A single strain of agent appears to be responsible for all vCJD cases to date

Measuring neural excitation and inhibition in autism: different approaches, different findings and different interpretations.

The balance of neural excitation and inhibition (E/I balance) is often hypothesised to be altered in autism spectrum disorder (ASD). One widely held view is that excitation levels are elevated relative to inhibition in ASD. Understanding whether, and how, E/I balance may be altered in ASD is important given the recent interest in trialling pharmacological interventions for ASD which target inhibitory neurotransmitter function. Here we provide a critical review of evidence for E/I balance in ASD. We conclude that data from a number of domains provides support for alteration in excitation and inhibitory neurotransmission in ASD, but when considered collectively, the available literature provide little evidence to support claims for either a net increase in excitation or a net increase in inhibition. Strengths and limitations of available techniques are considered, and directions for future research discussed

TNF-α mediated keratinocyte expression and release of matrix metalloproteinase 9: putative mechanism of pathogenesis in Stevens-Johnson syndrome/ toxic epidermal necrolysis.

Stevens Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are severe cutaneous adverse drug reactions (ADRs) characterised by widespread keratinocyte cell-death and epidermal detachment. At present, there is little understanding of how the detachment occurs or how it is abrogated by the TNF-α inhibitor etanercept, an effective SJS/TEN treatment. RNA-sequencing was used to identify upregulated transcripts in formalin-fixed paraffin-embedded SJS/TEN skin biopsies. Epidermal matrix metalloproteinase 9 (MMP9) expression was assessed by immunohistochemistry in skin biopsies and cultured human skin explants exposed to serum from cutaneous ADRs patients. TNF-α-induced MMP9 expression and activity, and its abrogation by etanercept was determined using the HaCaT immortalised keratinocyte cell-line. Epidermal MMP9 expression was significantly higher in SJS/TEN skin (70.6%) vs. healthy control skin (0%, p=0.0098) and non-bullous skin reactions (10.7%, p=0.0002). SJS/TEN serum induced significant MMP9 expression and collagenase activity in healthy skin explants, which was reduced by etanercept. Etanercept was also able negate the TNF-α induced MMP9 expression in the HaCaT cell line. Data suggest that elevated epidermal MMP9 expression and collagenase activity is a putative pathogenic mechanism in SJS/TEN, which is limited by etanercept. Modulation of MMP9 expression and activity represents to our knowledge a previously unreported therapeutic target for the treatment of SJS/TEN

Synergistic ecoclimate teleconnections from forest loss in different regions structure global ecological responses

ABSTRACT: Forest loss in hotspots around the world impacts not only local climate where loss occurs, but also influences climate and vegetation in remote parts of the globe through ecoclimate teleconnections. The magnitude and mechanism of remote impacts likely depends on the location and distribution of forest loss hotspots, but the nature of these dependencies has not been investigated. We use global climate model simulations to estimate the distribution of ecologically-relevant climate changes resulting from forest loss in two hotspot regions: western North America (wNA), which is experiencing accelerated dieoff, and the Amazon basin, which is subject to high rates of deforestation. The remote climatic and ecological net effects of simultaneous forest loss in both regions differed from the combined effects of loss from the two regions simulated separately, as evident in three impacted areas. Eastern South American Gross Primary Productivity (GPP) increased due to changes in seasonal rainfall associated with Amazon forest loss and changes in temperature related to wNA forest loss. Eurasia’s GPP declined with wNA forest loss due to cooling temperatures increasing soil ice volume. Southeastern North American productivity increased with simultaneous forest loss, but declined with only wNA forest loss due to changes in VPD. Our results illustrate the need for a new generation of local-to-global scale analyses to identify potential ecoclimate teleconnections, their underlying mechanisms, and most importantly, their synergistic interactions, to predict the responses to increasing forest loss under future land use change and climate change

Process account of curiosity and interest: a reward-learning perspective

Previous studies suggested roles for curiosity and interest in knowledge acquisition and exploration, but there has been a long-standing debate about how to define these concepts and whether they are related or different. In this paper, we address the definition issue by arguing that there is inherent difficulty in defining curiosity and interest, because both curiosity and interest are naïve concepts, which are not supposed to have a priori scientific definitions. We present a reward-learning framework of autonomous knowledge acquisition and use this framework to illustrate the importance of process account as an alternative to advance our understanding of curiosity and interest without being troubled by their definitions. The framework centers on the role of rewarding experience associated with knowledge acquisition and learning and posits that the acquisition of new knowledge strengthens the value of further information. Critically, we argue that curiosity and interest are the concepts that they subjectively construe through this knowledge-acquisition process. Finally, we discuss the implications of the reward-learning framework for education and empirical research in educational psychology