One-hundred-and-forty-five unmarked graves were accidentally uncovered outside the Gladstone cemetery in Kimberley, South Africa, in 2003. This study aimed to describe the archaeological findings,... Show moreOne-hundred-and-forty-five unmarked graves were accidentally uncovered outside the Gladstone cemetery in Kimberley, South Africa, in 2003. This study aimed to describe the archaeological findings, demographic composition and health of the unknown human remains excavated from the site. Fifteen graves containing 107 skeletons were exhumed from the trench and analyzed using standard anthropometric techniques. Archaeological and palaeopathological evidence suggested that the remains were most likely those of migrant mine workers who died between 1897 and 1900, with the majority of the population consisting of young male individuals of low socio-economic status. The prevalence of infectious diseases observed in the sample, most likely reflects the pre-antibiotic era from which these individuals came as well as the overcrowded and unhygienic living conditions to which they were exposed on a daily basis. High frequencies of cranial and long bone fractures observed are testimony to the high levels of interpersonal violence and hazardous mining environment described in archival documents. Other pathological lesions such as spondylolysis, Schm_rl's nodes and enthesophytes are possibly indicative of the physical demands associated with mining activities. These results support reports describing the appalling conditions and hazards to which migrant mine workers were exposed to in the late nineteenth century Show less
A data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was... Show moreA data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was applied to clinical research on Osteoarthritis (OA) and Parkinson’s disease (PD) and in drug discovery. Thus, because OA and PD are characterized by clinical heterogeneity, a more sensitive classification of the cohort of patients may contribute to the search for the underlying diseases mechanism. In drug discovery, subtyping may improve the understanding of the similarity (and distance) between different phenotypic effects as induced by drugs and chemicals. Our second scenario aims to compare text classification algorithms. First, we show that common classifiers achieve comparable performance on most problems. Second, tightly constrained SVM solutions are high performers. In that situation, most training documents are bounded support vectors, SVM reduces to a nearest mean classifier and no training is necessary, which raises a question on SVM merits in sparse bag of words feature spaces. Also, SVM is shown to suffer from performance deterioration for particular combinations of training set size/number of features. This relate to outlying documents of distinct classes overlapping in the feature space. Show less
