Manchester Metropolitan University

About: Manchester Metropolitan University is a education organization based out in Manchester, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5435 authors who have published 16202 publications receiving 442561 citations. The organization is also known as: Manchester Polytechnic & MMU.

Retention of Candida albicans on acrylic resin and silicone of different surface topography.

Abstract: Statement of problem. The adhesion of microorganisms to a denture surface is a prerequisite for colonization. Purpose. This study compared the retention of Candida albicans on smooth and rough acrylic resin and silicone surfaces after a washing procedure to determine the effect of surface roughness on prosthesis infection and hygiene. Material and methods. Standardized cell suspensions of C. albicans were incubated with smooth and rough acrylic resin and silicone surfaces for 1 hour at 24° C. After washing, cells that had been retained on the surface were stained with acridine orange and examined with incident beam fluorescent microscopy. Results. There was no significant difference in cell numbers on either of the smooth surfaces. Significantly higher numbers of cells ( p >0.0005) were observed on roughened surfaces (silicone > acrylic resin) than on smooth surfaces. The fitting surface of the maxillary denture was not polished. Conclusions. Silicones used in prostheses were processed against dental stone. The resultant surface roughness may facilitate microbial retention and infection and should therefore be kept to a minimum. (J Prosthet Dent 1997;77:535-9.)

Testing the Generality of Bird‐Habitat Models

Abstract: Bird-habitat models are frequently used as predictive modeling tools—for example, to predict how a species will respond to habitat modifications. We investigated the generality of the predictions from this type of model. Multivariate models were developed for Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland. Data were obtained for all habitat and nest locations within an area of 2349 km2. This assemblage of species is relatively static with respect to both occupancy and spatial positioning. The area was split into five geographic subregions: two on the mainland and three on the adjacent Island of Mull, which has one of United Kingdom’s richest raptor fauna assemblages. Because data were collected for all nest locations and habitats, it was possible to build models that did not incorporate sampling error. A range of predictive models was developed using discriminant analysis and logistic regression. The models differed with respect to the geographical origin of the data used for model development. The predictive success of these models was then assessed by applying them to validation data. The models showed a wide range of predictive success, ranging from only 6% of nest sites correctly predicted to 100% correctly predicted. Model validation techniques were used to ensure that the models’ predictions were not statistical artefacts. The variability in prediction success seemed to result from methodological and ecological processes, including the data recording scheme and interregional differences in nesting habitat. The results from this study suggest that conservation biologists must be very careful about making predictions from such studies because we may be working with systems that are inherently unpredictable. Los modelos de habitat para aves han sido usados frecuentemente como herramientas predictivas de modelaje, por ejemplo, para predecir como una especie va a responder a modificaciones en el habitat. En el presente estudio investigamos la generalidad de las predicciones hechas por este tipo de modelos. Modelos multivariados fueron desarrollados para las aguilas dorados (Aquila chrysaetos), los cuervos (Corvus corax) y los buitres (Buteo buteo) que habitan el noroeste de Escocia. Se obtuvieron datos para todos los habitat y sitios con nidos dentro de un area de 2349 km2. Este conjunto de especies es relativamente estatico con respectio a su posesion y posicion espacial. El area fue dividida en cinco subregiones geograficas; dos en tierra firme y tres en las islas adyacentes de Mull que poseen una de las asociaciones de fauna de aves de rapina mas ricas del Reino Unido. Debido a que se recolectaron datos de todos los sitios con nidos y habitats, fue posible construir modelos que no incorporaron errores de muestreo. Se desarrollo una serie de modelos predictivos usando analisis discriminante y regresiones logisticas. Los modelos difirieron en lo que respecta al origen geografico de los datos usados en el desarrollo del modelo. El exito predictivo de estos modelos fue evaluado aplicandolos a datos de validacion. Los modelos variaron ampliamente en su exito predictivo, con una prediccion correcta de los sitios con nidos que vario entre un 6% y un 100%. Se utilizaron tecnicas de validacion de modelos que aseguraron que las predicciones de los modelos no eran artificios estadisticos. La variabilidad en el exito predictivo parecio ser el resultado de procesos metodologicos y ecologicos que incluyeron el esquema de registro de los datos y diferencias interregionales en los habitats de anidacion. Los resultados de este estudio sugieren que los biologos de conservacion tienen que ser muy cuidadosos la al hacer predicciones a partir de tales estudios porque se podria estar trabajando con sistemas que son inherentemente impredecibles.

Experimental and numerical assessment of the shallow water model for two-dimensional dam-break type problems

Abstract: Experimental and numerical results concerning the flow induced by the break of a dam on a dry bed are presented. The numerical technique consists of a shock-capturing method of the Godunov type. A physical laboratory model has been employed to infer properties and validity of the numerical solution. Attention is also given to the applicability of the mathematical model, based on the shallow water equations, to this class of problems.