Publications by authors named "E W Garren"

The filterscope diagnostic on DIII-D utilizes photomultiplier tubes to measure visible light emission from the plasma. The system has undergone a substantial upgrade since previous attempts to cross-calibrate the filterscope with other spectroscopic diagnostics were unsuccessful. The optics now utilize a dichroic mirror to initially split the light at nearly 99% transmission or reflectance for light below or above 550 nm.

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Abundant evidence supports the presence of at least three distinct types of thalamocortical (TC) neurons in the primate dorsal lateral geniculate nucleus (dLGN) of the thalamus, the brain region that conveys visual information from the retina to the primary visual cortex (V1). Different types of TC neurons in mice, humans, and macaques have distinct morphologies, distinct connectivity patterns, and convey different aspects of visual information to the cortex. To investigate the molecular underpinnings of these cell types, and how these relate to differences in dLGN between human, macaque, and mice, we profiled gene expression in single nuclei and cells using RNA-sequencing.

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A diagnostic system, which has a design goal of high-portability, has been designed at Oak Ridge National Laboratory (ORNL). This project aims at providing measurements of key plasma parameters (n, T, n, T) for fusion-relevant devices, utilizing Thomson scattering (TS) and optical emission spectroscopy (OES). The innovative design employs mostly commercial off-the-shelf instrumentation and a traveling team of researchers to conduct measurements at various magnetic-confinement plasma devices.

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Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D.

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