541 research outputs found
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
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Establishment and characterization of two novel patient-derived lines from canine high-grade glioma.
High-grade glioma is an aggressive cancer that occurs naturally in pet dogs. Canine high-grade glioma (cHGG) is treated with radiation, chemotherapy or surgery, but has no curative treatment. Within the past eight years, there have been advances in our imaging and histopathology standards as well as genetic charactereization of cHGG. However, there are only three cHGG cell lines publicly available, all of which were derived from astrocytoma and established using methods involving expansion of tumour cells in vitro on plastic dishes. In order to provide more clinically relevant cell lines for studying cHGG in vitro, the goal of this study was to establish cHGG patient-derived lines, whereby cancer cells are expanded in vivo by injecting cells into immunocompromized laboratory mice. The cells are then harvested from mice and used for in vitro studies. This method is the standard in the human field and has been shown to minimize the acquisition of genetic alterations and gene expression changes from the original tumour. Through a multi-institutional collaboration, we describe our methods for establishing two novel cHGG patient-derived lines, Boo-HA and Mo-HO, from a high-grade astrocytoma and a high-grade oligodendroglioma, respectively. We compare our novel lines to G06-A, J3T-Bg, and SDT-3G (traditional cHGG cell lines) in terms of proliferation and sensitivity to radiation. We also perform whole genome sequencing and identify an NF1 truncating mutation in Mo-HO. We report the characterization and availability of these novel patient-derived lines for use by the veterinary community
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
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