Art, The First Amendment, and the NEA Controversy

America has been caught up in a struggle between those who support values rooted in Judeo-Christian morality and those who would discard those values in favor of a radical moral relativism

Extracellular Matrix Influences Alveolar Epithelial Claudin Expression and Barrier Function

The lung is dynamically remodeled in response to injury, which alters extracellular matrix composition, and can lead to either healthy or impaired lung regeneration. To determine how changes in extracellular matrix can influence alveolar epithelial barrier function, we examined the expression and function of tight junction proteins by rat alveolar epithelial type II cells cultured on one of three different matrix components: type I collagen or fibronectin, matrix glycoproteins which are highly expressed in injured lungs, or laminin, a basement membrane matrix component. Of note, alveolar epithelial cells cultured for 2 days on fibronectin formed high-resistance barriers and showed continuous claudin-3 and claudin-18 localization to the plasma membrane, as opposed to cells cultured on either type I collagen or laminin, which had low resistance monolayers and had areas of cell–cell contact that were claudin deficient. The barrier formed by cells cultured on fibronectin also had preferential permeability to chloride as compared with sodium. Regardless of the initial matrix composition, alveolar epithelial cells cultured for 5 days formed high-resistance barriers, which correlated with increased claudin-18 localization to the plasma membrane and an increase in zonula occludens-1. Day 5 cells on laminin had significantly higher resistance than cells on either fibronectin or type I collagen. Thus, although alveolar epithelial cells on fibronectin formed rapid barriers, it was at the expense of producing an optimized barrier

Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging

<div><p>Rationale</p><p>Disruptions of brain anatomical connectivity are believed to play a central role in several neurological and psychiatric illnesses. The structural brain connectome is typically derived from diffusion tensor imaging (DTI), which may be influenced by methodological factors related to signal processing, MRI scanners and biophysical properties of neuroanatomical regions. In this study, we evaluated how these variables affect the reproducibility of the structural connectome.</p><p>Methods</p><p>Twenty healthy adults underwent 3 MRI scanning sessions (twice in the same MRI scanner and a third time in a different scanner unit) within a short period of time. The scanning sessions included similar T1 weighted and DTI sequences. Deterministic or probabilistic tractography was performed to assess link weight based on the number of fibers connecting gray matter regions of interest (ROI). Link weight and graph theory network measures were calculated and reproducibility was assessed through intra-class correlation coefficients, assuming each scanning session as a rater.</p><p>Results</p><p>Connectome reproducibility was higher with data from the same scanner. The probabilistic approach yielded larger reproducibility, while the individual variation in the number of tracked fibers from deterministic tractography was negatively associated with reproducibility. Links connecting larger and anatomically closer ROIs demonstrated higher reproducibility. In general, graph theory measures demonstrated high reproducibility across scanning sessions.</p><p>Discussion</p><p>Anatomical factors and tractography approaches can influence the reproducibility of the structural connectome and should be factored in the interpretation of future studies. Our results demonstrate that connectome mapping is a largely reproducible technique, particularly as it relates to the geometry of network architecture measured by graph theory methods.</p></div

Average connectivity matrices from all subjects for each scanning session (Time 1 and 2 in Scanner A represent measures from the same MRI scanner in different time points, and Time 1 in Scanner B represents a third measure from a different MRI scanner).

<p>Each matrix element represents the weighted connectivity between the ROIs indicated by the column and by the row. The color bars indicate log(weighted connectivity).</p

The scatter plots demonstrate the relationship between link-wise graph theory metrics obtained from connectomes calculated from scanning session in time 1 (x-axis) and in time 2 (y-axis) within the same MRI scanner.

<p>The scale set for the x-axis is the same as for the y-axis for all graphs. The ICC between each pair of measurements is displayed below each scatter plot. Of note, the relationship between degrees was not assessed for probabilistic tractography given the low sparsity of networks generated from probabilistic methods, therefore leading to a ceiling degree effect.</p

The scatter plots demonstrate the relationship between link-wise graph theory metrics across different scanners (Time 1, Scanner in x-axis and Time 1 Scanner B in y-axis).

<p>The ICC between each pair of measurements is displayed below each scatter plot. Similarly, the relationship between degrees was not assessed for probabilistic tractography given the low sparsity of networks generated from probabilistic methods, therefore leading to a ceiling degree effect.</p