A novel role for murine IL-4 in vivo: induction of MUC5AC gene expression and mucin hypersecretion

Mucus hypersecretion and plugging of lower respiratory tract airways contributes to the morbidity and mortality associated with asthma. Interleukin (IL)-4 plays a putative role in some forms of asthma. Thus, transgenic mice that overexpress murine IL-4 selectively within the lung were used to study the effect of IL-4 on mucus glycoprotein gene expression and mucin release. Histologic examination of lung sections from IL-4 mice revealed that nonciliated epithelial cells from conducting airways were hypertrophic, due at least in part to the accumulation of mucus glycoprotein. The cytoplasm of these cells stained positively for glycoproteins using mucicarmine, alcian blue (AB), and periodic acid-Schiff (PAS). Ciliated cells were also enlarged but did not show any mucin-specific staining. Inclusion granules typically found in nonciliated (Clara) cells of control mice were absent in the IL-4 transgenic mice. Northern blot analysis of total RNA from lung tissue revealed that the expression of the MUC5AC, but not MUC2, mucin gene was distinctly upgraded in IL-4 transgenic mice compared to transgene-negative controls. In addition, a 5- to 10-fold increase in AB- and PAS-positive material was found in lavage fluid from IL-4 overexpressing mice compared to transgene-negative controls. Thus, the overexpression of IL-4 locally within the lung enhances mucus glycoprotein synthesis by altering gene expression, results in the accumulation of mucus glycoprotein in nonciliated epithelial cells, and induces the release of mucus into the airway lumen. We therefore hypothesize that the overproduction of mucus seen in some patients with asthma may be a direct result of the action of IL-4 within the inflamed lung.     

Probabilistic Models of Appearance for 3-D Object Recognition

We describe how to model the appearance of a 3-D object using multiple views, learn such a model from training images, and use the model for object recognition. The model uses probability distributions to describe the range of possible variation in the object's appearance. These distributions are organized on two levels. Large variations are handled by partitioning training images into clusters corresponding to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions characterizing uncertainty in the presence, position, and measurements of various discrete features of appearance. Many types of features are used, ranging in abstraction from edge segments to perceptual groupings and regions. A matching procedure uses the feature uncertainty information to guide the search for a match between model and image. Hypothesized feature pairings are used to estimate a viewpoint transformation taking account of feature uncertainty. These methods have been implemented in an object recognition system, OLIVER. Experiments show that OLIVER is capable of learning to recognize complex objects in cluttered images, while acquiring models that represent those objects using relatively few views.     

Fitting parameterized three-dimensional models to images

Model-based recognition and motion tracking depend upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. The author extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulation, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The Levenberg-Marquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects     

Hierarchically coupled ultradian oscillators generating robust circadian rhythms

Ensembles of mutually coupled ultradian cellular oscillators have been proposed by a number of authors to explain the generation of circadian rhythms in mammals. Most mathematical models using many coupled oscillators predict that the output period should vary as the square root of the number of participating units, thus being inconsistent with the well-established experimental result that ablation of substantial parts of the suprachiasmatic nuclei (SCN), the main circadian pacemaker in mammals, does not eliminate the overt circadian functions, which show no changes in the phases or periods of the rhythms. From these observations, we have developed a theoretical model that exhibits the robustness of the circadian clock to changes in the number of cells in the SCN, and that is readily adaptable to include the successful features of other known models of circadian regulation, such as the phase response curves and light resetting of the phase.