Sleep deprivation of rats: The hyperphagic response is real

Study Objectives: Chronic sleep deprivation of rats causes hyperphagia without body weight gain. Sleep deprivation hyperphagia is prompted by changes in pathways governing food intake; hyperphagia may be adaptive to sleep deprivation hypermetabolism. A recent paper suggested that sleep deprivation might inhibit ability of rats to increase food intake and that hyperphagia may be an artifact of uncorrected chow spillage. To resolve this, a palatable liquid diet (Ensure) was used where spillage is insignificant. Design: Sleep deprivation of male Sprague Dawley rats was enforced for 10 days by the flowerpot/platform paradigm. Daily food intake and body weight were measured. On day 10, rats were transcardially perfused for analysis of hypothalamic mRNA expression of the orexigen, neuropeptide Y (NPY). Setting: Morgan State University, sleep deprivation and transcardial perfusion; University of Maryland, NPY in situ hybridization and analysis. Measurements and Results: Using a liquid diet for accurate daily measurements, there was no change in food intake in the first 5 days of sleep deprivation. Importantly, from days 6-10 it increased significantly, peaking at 29% above baseline. Control rats steadily gained weight but sleep-deprived rats did not. Hypothalamic NPY mRNA levels were positively correlated to stimulation of food intake and negatively correlated with changes in body weight. Conclusion: Sleep deprivation hyperphagia may not be apparent over the short term (i.e., <= 5 days), but when extended beyond 6 days, it is readily observed. The timing of changes in body weight and food intake suggests that the negative energy balance induced by sleep deprivation prompts the neural changes that evoke hyperphagia.

A Framework for Exploring Multidimensional Data with 3D Projections

Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework`s applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping

The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analyses of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections ( LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in mD. In order to perform the projection, a small number of distance calculations are necessary, and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in 2D. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high-quality methods, particularly where it was mostly tested, that is, for mapping text sets.