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Wayne A Moore

Publications and source records attributed to Wayne A Moore.

5 recordsLinked to original sources
PubMed2006

Effects of artificial rearing on contractile properties of genioglossus muscle in Sprague-Dawley rat.

Mammals suckle from a nipple during the early neonatal period to obtain nourishment. The genioglossus muscle helps position and move the tongue for efficient suckling. The purpose of this study was to examine the contractile properties and myosin heavy chain (MHC) phenotype of the genioglossus following an early period of artificial rearing, which reduced nutritive suckling. Beginning at 3 days of age, rats were fed via gastric cannula until postnatal day 14 (P14). At P14, artificially reared rat pups were either allowed to grow to postnatal day 42 (P42) or anaesthetised and prepared for experimentation. Comparisons were made between artificially reared and dam reared groups at P14 and P42. At P14 maximum tetanic tension and fatigue index were lower in the artificially reared group than the dam reared group. By P42, artificially reared rats had a higher fatigue index and lower percentage of MHCIIa than dam reared rats. The artificial rearing technique employed in this study was adequate to produce chronic changes in fatigue resistance and MHC distribution in genioglossus muscle of rat; the changes observed here may be similar to changes that occur in premature human infants requiring early artificial feedings.

Animals↗
PubMed2006

A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data.

BACKGROUND: In immunofluorescence measurements and most other flow cytometry applications, fluorescence signals of interest can range down to essentially zero. After fluorescence compensation, some cell populations will have low means and include events with negative data values. Logarithmic presentation has been very useful in providing informative displays of wide-ranging flow cytometry data, but it fails to adequately display cell populations with low means and high variances and, in particular, offers no way to include negative data values. This has led to a great deal of difficulty in interpreting and understanding flow cytometry data, has often resulted in incorrect delineation of cell populations, and has led many people to question the correctness of compensation computations that were, in fact, correct. RESULTS: We identified a set of criteria for creating data visualization methods that accommodate the scaling difficulties presented by flow cytometry data. On the basis of these, we developed a new data visualization method that provides important advantages over linear or logarithmic scaling for display of flow cytometry data, a scaling we refer to as "Logicle" scaling. Logicle functions represent a particular generalization of the hyperbolic sine function with one more adjustable parameter than linear or logarithmic functions. Finally, we developed methods for objectively and automatically selecting an appropriate value for this parameter. CONCLUSIONS: The Logicle display method provides more complete, appropriate, and readily interpretable representations of data that includes populations with low-to-zero means, including distributions resulting from fluorescence compensation procedures, than can be produced using either logarithmic or linear displays. The method includes a specific algorithm for evaluating actual data distributions and deriving parameters of the Logicle scaling function appropriate for optimal display of that data. It is critical to note that Logicle visualization does not change the data values or the descriptive statistics computed from them.

Algorithms↗
PubMed2004

New approaches to fluorescence compensation and visualization of FACS data.

The Fluorescence Activated Cell Sorter (FACS) is an invaluable tool for clinicians and researchers alike in phenotyping and sorting individual cells. With the advances in FACS methodology, notably intracellular staining for cytokines, transcription factors and phosphoproteins, and with increases in the number of fluorescence detection channels, researchers now have the opportunity to study individual cells in far greater detail than previously possible. In this chapter, we discuss High-Definition (Hi-D) FACS methods that can improve analysis of lymphocyte subsets in mouse and man. We focus on the reasons why fluorescence compensation, which is necessary to correct for spectral overlap between two or more fluorochromes used in the same staining combination, is best done as a computed transformation rather than using the analog circuitry available on many flow cytometers. In addition, we introduce a new data visualization method that scales the axes on histograms and two-dimensional contour (or dot) plots to enable visualization of signals from all cells, including those that have minimal fluorescence values and are not properly represented with traditional logarithmic axes. This "Logicle" visualization method, we show, provides superior representations of compensated data and makes correctly compensated data look correct. Finally, we discuss controls that facilitate recognition of boundaries between positive and negative subsets.

Animals↗
PubMed2004

Identification of B-cell subsets: an exposition of 11-color (Hi-D) FACS methods.

In the last few years, the effectiveness of developmental and functional studies of individual subsets of cells has increased dramatically owing to the identification of additional subset markers and the extension of fluorescence-activated cell sorter (FACS) capabilities to simultaneously measure the expression of more markers on individual cells. For example, introduction of a 6-8 multiparameter FACS instrument resulted in significant advances in understanding B-cell development. In this chapter, we describe 11-color high-dimensional (Hi-D) FACS staining and data analysis methods that provide greater clarity in identifying the B-cell subsets in bone marrow, spleen, and peritoneal cavity. Further, we show how a single Hi-D FACS antibody reagent combination is sufficient to unambiguously identify most of the currently defined B-cell developmental subsets in the bone marrow (Hardy fractions A-F) and the functional B-cell subsets (B-1a, B-1b, B-2, and marginal zone [MZ] B cells) in the periphery. Although we focus on murine B-cell subsets, the methods we discuss are relevant to FACS studies conducted with all types of cells and other FACS instruments. We introduce a new method for scaling axes for histograms or contour plots of FACS data. This method, which we refer to as Logicle visualization, is particularly useful in promoting correct interpretations of fluorescence-compensated FACS data and visual confirmation of correct compensation values. In addition, it facilitates discrimination of valid subsets. Application of Logicle visualization tools in the Hi-D FACS studies discussed here creates a strong new base for in-depth analysis of B-cell development and function.

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