Staphylococcus aureus is an important cause of bone and joint infections. In recent years, significant changes in the incidence of septic arthritis and osteomyelitis have occurred. Haematogenous osteomyelitis is now less common during childhood, but secondary spread of infection to bone or joint from a contiguous site in adults is increasing in incidence. Infection introduced at the time of surgery or arising by the haematogenous route is a significant complication of prosthetic joint implantation, and the effect of bone cement on local immune function may be important in this setting. Although S. epidermidis is a more common cause of prosthetic joint infection, S. aureus is more difficult to treat. S. aureus produces a number of extracellular and cellassociated factors, but it is unclear what role these have as virulence factors in vivo. Furthermore, it is difficult in animal models to simulate transient bacteraemia followed by non-fulminating septic arthritis or osteomyelitis, as occurs in the patient. Surface factors which may be important in pathogenesis include the cell wall (activates complement and stimulates cytokine release), capsular polysaccharide (promotes adhesion to host cell surfaces), collagen receptors and fibronectin-binding protein.Staphylococcal toxic shock syndrome toxin (TSST-1) and the enterotoxins are superantigens and have the potential to suppress plasma cell differentiation and antibody responsiveness. TSST-1-positive isolates have been shown to cause more severe joint infection in one animal model, but most other studies to date have focused on in-vitro rather than in-vivo effects. There is little evidence supporting a role for coagulase, lipase and the haemolysins in staphylococcal bone and joint infections. Despite the clinical importance of these infections, surprisingly little is known about pathogenesis at the cellular level. Future research should focus on the role of the host immune system in limiting spread of infection, and the expression of virulence factors in animal or other models incorporating isogenic mutant strains.
Spectral reflectance curves of selected soil characterization samples and standard clay minerals were studied over the wavelength interval 0.5 to 2.6µm. Results reported include reflectance data from three Hagerstown profiles and the Ap horizons of a Berks, Penn, and Ellery profile. Hagerstown soils are developed from limestone while the Berks, Penn, and Ellery are developed from shale, sandstone, and glacial deposits, respectively. Spectral curves for standard clay mineral samples of nontronite, kaolinite, and illite are also included.Spectral reflectance data indicate that clay type and the amount of organic matter, free iron oxides and silt influence the intensity of energy reflected by soils in the 0.5 to 2.6µm range. High contents of organic matter and free iron oxides reduced reflectance intensity in the 0.5‐to‐1.2µm range while clay type influenced curve shape and intensity over the entire range studied. For the Hagerstown profiles silt is highly correlated with percent reflectance in the 0.8‐to‐2.5µm range.
The soil‐landscape relationships frequently used for soil mapping activities are seldom documented. We developed a statistical model that relates soil drainage classes to eight landscape parameters describing slope morphology, proximity to surface drainage features, and soil parent material. Soil profiles and landscape parameters were described at 305 randomly selected sampling points within the Mifflintown 7.5‐min topographic quadrangle in the unglaciated ridge and valley physiographic province of central Pennsylvania. Variables defining the spatial structure of the landscape were derived from digitized 1:24 000 scale maps of streams and drainageways, surficial geology, bedrock geology, and a digital elevation model. These data were stored in a geographic information system and overlaid with the sampling point locations to define a database of 305 known soil‐landscape combinations. These soil‐landscape combinations were used to derive a statistical soil‐landscape model using multivariate discriminant analysis and class frequency information. For soil drainage class, a 74% overall agreement with field observations was found for the model using a cross‐validation approach, compared with 69% for the published soil survey. The model correctly predicted a majority of the observations within each drainage class and provided a consistent method of extrapolating point information about soils to the three‐dimensional landscape.
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