Dust and grit matter: abrasives of different size lead to opposing dental microwear textures in experimentally fed sheep (Ovis aries)

External abrasives ingested along with the herbivore diet are considered main contributors to dental wear, though how the different sizes and concentrations of these abrasives influence wear remains unclear. Dental microwear texture analysis (DMTA) is an establishedmethod for dietary reconstructionwhich describes a tooth’s surface topography on a micrometre scale. The method has yielded conflicting results as to the effect of external abrasives. In the present study, a feeding experiment was performed on sheep (Ovis aries) fed seven diets of different abrasiveness. Our aim was to discern the individual effects of size (4, 50 and 130 μm) and concentration (0%,4% and 8% of dry matter) of abrasives on dental wear, applying DMTA to four tooth positions. Microwear textures differed between individual teeth, but surprisingly, showed no gradient along the molar tooth row, and the strongest differentiation of experimental groups was achieved when combining data of all maxillary molars. Overall, a pattern of increasing height, volume and complexity of the tooth’s microscopic surface appeared with increasing size of dietary abrasives, and when compared with the control, the small abrasive diets showed a polishing effect. The results indicate that the size of dietary abrasives is more important for dental microwear texture traces than their concentration, and that different sizes can have opposing effects on the dietary signal. The latter finding possibly explains conflicting evidence from previous experimental DMTA applications. Further exploration is required to understand whether and how microscopic traces created by abrasives translate quantitatively to tissue loss

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms