Microstructural Modeling of Collagen Network Mechanics and Interactions with the Proteoglycan Gel in Articular Cartilage

Cartilage matrix mechanical function is largely determined by interactions between the collagen fibrillar network and the proteoglycan gel. Although the molecular physics of these matrix constituents have been characterized and modern imaging methods are capable of localized measurement of molecular densities and orientation distributions, theoretical tools for using this information for prediction of cartilage mechanical behavior are lacking. We introduce a means to model collagen network contributions to cartilage mechanics based upon accessible microstructural information (fibril density and orientation distributions) and which self-consistently follows changes in microstructural geometry with matrix deformations. The interplay between the molecular physics of the collagen network and the proteoglycan gel is scaled up to determine matrix material properties, with features such as collagen fibril pre-stress in free-swelling cartilage emerging naturally and without introduction of ad hoc parameters. Methods are developed for theoretical treatment of the collagen network as a continuum-like distribution of fibrils, such that mechanical analysis of the network may be simplified by consideration of the spherical harmonic components of functions of the fibril orientation, strain, and stress distributions. Expressions for the collagen network contributions to matrix stress and stiffness tensors are derived, illustrating that only spherical harmonic components of orders 0 and 2 contribute to the stress, while orders 0, 2, and 4 contribute to the stiffness. Depth- and compression-dependent equilibrium mechanical properties of cartilage matrix are modeled, and advantages of the approach are illustrated by exploration of orientation and strain distributions of collagen fibrils in compressed cartilage. Results highlight collagen-proteoglycan interactions, especially for very small physiological strains where experimental data are relatively sparse. These methods for determining matrix mechanical properties from measurable quantities at the microscale (composition, structure, and molecular physics) may be useful for investigating cartilage structure-function relationships relevant to load-bearing, injury, and repair.     

A rangewide population genetic study of trumpeter swans

For management purposes, the range of naturally occurring trumpeter swans (Cygnus buccinator) has been divided into two populations, the Pacific Coast Population (PP) and the Rocky Mountain Population (RMP). Little is known about the distribution of genetic variation across the species’ range despite increasing pressure to make difficult management decisions regarding the two populations and flocks within them. To address this issue, we used rapidly evolving genetic markers (mitochondrial DNA sequence and 17 nuclear microsatellite loci) to elucidate the underlying genetic structure of the species. Data from both markers revealed a significant difference between the PP and RMP with the Yukon Territory as a likely area of overlap. Additionally, we found that the two populations have somewhat similar levels of genetic diversity (PP is slightly higher) suggesting that the PP underwent a population bottleneck similar to a well-documented one in the RMP. Both genetic structure and diversity results reveal that the Tri-State flock, a suspected unique, non-migratory flock, is not genetically different from the Canadian flock of the RMP and need not be treated as a unique population from a genetic standpoint. Finally, trumpeter swans appear to have much lower mitochondrial DNA variability than other waterfowl studied thus far which may suggest a previous, species-wide bottleneck.     

Neuromechanics: an integrative approach for understanding motor control

Neuromechanics seeks to understand how muscles, sense organs,motor pattern generators, and brain interact to produce coordinatedmovement, not only in complex terrain but also when confrontedwith unexpected perturbations. Applications of neuromechanicsinclude ameliorating human health problems (including prosthesisdesign and restoration of movement following brain or spinalcord injury), as well as the design, actuation and control ofmobile robots. In animals, coordinated movement emerges fromthe interplay among descending output from the central nervoussystem, sensory input from body and environment, muscle dynamics,and the emergent dynamics of the whole animal. The inevitablecoupling between neural information processing and the emergentmechanical behavior of animals is a central theme of neuromechanics.Fundamentally, motor control involves a series of transformationsof information, from brain and spinal cord to muscles to body,and back to brain. The control problem revolves around the specifictransfer functions that describe each transformation. The transferfunctions depend on the rules of organization and operationthat determine the dynamic behavior of each subsystem (i.e.,central processing, force generation, emergent dynamics, andsensory processing). In this review, we (1) consider the contributionsof muscles, (2) sensory processing, and (3) central networksto motor control, (4) provide examples to illustrate the interplayamong brain, muscles, sense organs and the environment in thecontrol of movement, and (5) describe advances in both roboticsand neuromechanics that have emerged from application of biologicalprinciples in robotic design. Taken together, these studiesdemonstrate that (1) intrinsic properties of muscle contributeto dynamic stability and control of movement, particularly immediatelyafter perturbations; (2) proprioceptive feedback reinforcesthese intrinsic self-stabilizing properties of muscle; (3) controlsystems must contend with inevitable time delays that can simplifyor complicate control; and (4) like most animals under a varietyof circumstances, some robots use a trial and error processto tune central feedforward control to emergent body dynamics.     

A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow.

BACKGROUND: Cellular binding of annexin V and membrane permeability to 7-aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or "gates," on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow. METHODS: Our method was developed for classification of viability using an in vitro cell system and then applied to an ex vivo analysis of murine late-stage erythroid progenitors. To induce apoptosis and cell death in vitro, an EPO-dependent human leukemic cell line, UT-7(EPO) cells were incubated without recombinant human erythropoietin (rhEPO) for 72 h. Five different ANNs were trained to recognize live, apoptotic, and dead cells using a "known" subset of the data for training, and a K-fold cross validation procedure for error estimation. The ANNs developed with the in vitro system were then applied to classify cells from an ex vivo study of rhEPO treated mice. Tg197 (human tumor necrosis-alpha transgenic mice, a model of anemia of chronic disease) received a single s.c. dose of 10,000 U/kg rhEPO and femoral bone marrow was collected 1, 2, 4, and 8 days after dosing. Femoral bone marrow cells were stained with TER-119 PE, CD71 APC enable identification of erythroid precursors, and annexin V FITC and 7AAD to identify the apoptotic and dead cells. During classification forward and side angle light scatter were also input to all pattern recognition systems. RESULTS: Similar decision boundaries between live, apoptotic, and dead cells were consistently identified by the neural networks. The best performing network was a radial basis function multi-perceptron that produced an estimated average error rate of 4.5% +/- 0.9%. Using these boundaries, the following results were reached: depriving UT-7(EPO) cells of rhEPO induced apoptosis and cell death while the addition of rhEPO rescued the cells in a dose-dependent manner. In vivo, treatment with rhEPO resulted in an increase of live erythroid cells in the bone marrow to 119.8% +/- 9.8% of control at the 8 day time point. However, a statistically significant transient increase in TER-119(+) CD71(+) 7AAD(+) dead erythroid precursors was observed at the 1 and 2 day time points with a corresponding decrease in TER-119(+) CD71(+) 7AAD(-) Annexin V(-) live erythroid precursors, and no change in the number of TER-119(+) CD71(+) annexin V(+) 7AAD(-) apoptotic erythroid precursors in the bone marrow. CONCLUSIONS: A statistical pattern recognition approach to viability classification provides an objective rationale for setting decision boundaries between "positive" and "negative" intensity measures in cytometric data. Using this approach we have confirmed that rhEPO inhibits apoptosis and cell death in an EPO dependent cell line in vitro, but failed to do so in vivo, suggesting EPO may not act as a simple antiapoptotic agent in the bone marrow. Rather, homeostatic mechanisms may regulate the pharmacodynamic response to rhEPO.