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As proporções na face e no corpo humano são analisadas desde a antiguidade, a exemplo do Homem Vitruviano de Leonardo da Vince. A existência das proporções Divinas ou proporções áureas em crânios esqueletizados é um campo de estudo que apresenta um potencial colaborador para as áreas de sobreposição de imagens, reconstrução facial e identificação forense. O objetivo deste trabalho é estabelecer parâmetros antropométricos regionais baseados nas proporções áureas, instituindo coeficientes e índices faciais específicos para a amostra composta por sessenta crânios esqueletizados (n=60), oriundos do Instituto Médico Legal da Bahia. As mensurações foram realizadas diretamente sobre o crânio utilizando paquímetros digitais. Foram selecionadas 14 medidas craniométricas diferentes e a partir delas foram calculados 06 relações horizontais, 04 verticais e 06 índices faciais. Os dados foram tabulados e descritos por valores de média e desvio padrão, utilizou-se o intervalo de confiança da média (p<0,05). A proporção áurea (1,618) não foi encontrada na população examinada, entretanto foi possível estabelecer índices faciais e coeficientes próprios para as medidas, construindo parâmetros antropométricos regionais baseados nas proporções estudadas. A proporção altura da cabeça em relação a altura da face (V-GN/N-GN) igual a 1,69 foi a variável que mais se aproximou do número de ouro e a relação entre a altura nasal ser igual a metade da altura da face (N-SN=N-GN/2) como valor de 0,92 foi a variável que mais se aproximou entre as igualdades. Admiti-se que a partir de uma medida é possível alcançar a outra utilizando os índices faciais e as retas de regressão calculadas. Este estudo fornece indicadores úteis, pois busca estabelecer proporções nos crânios que são reproduzíveis na face, contribuindo para o aperfeiçoamento das técnicas auxiliares de Reconstrução Facial e Sobreposição de Imagens em estudos futuros. / The proportions of the human face and body have been studied since, at least, Leonardo da Vinci and his Vitruvian Man. The existence of divine or Golden Ratios in human skulls may collaborate with Image Superposition, Facial Reconstruction and Forensics Identification. The goal of this study is establishing regional anthropometrics parameters based on golden ratios, using specifics facial coefficients and indexes for sixty skulls (n=60), obtained from Institute of Legal Medicine of Bahia, BRA. The measurements were done directly over the skull using a digital pachymeter. Fourteen different measurements were selected and, from them, 06 horizontals and 04 verticals relations and 06 facial indexes were calculated. The data was charted and described by average and standard deviation. An average confidence interval was used (p<0,05).The golden ratio (1,618) has not been found among the population analyzed, however was possible establishing facial indexes and coefficients for the measurements, building regional anthropometric parameters based on the studied proportions. The proportion height of the head related to the height of the face (V-GN/N-GN) equal to 1,69 was the closest variable to the golden number and the relation between nasal height being equal to half a height of the face (N-SN=N-GN/2) as 0,92 was the closest variable between equalities. Admitting that from one measurement to another can be achieved using the facial indexes facial and linear regression calculated. This study provides useful indicators since tries to establish proportions on the skull that can be reproducible on the face, contributing for the improvement of auxiliary techniques of Facial Reconstruction and Images Superposition in future studies.

As proporções na face e no corpo humano são analisadas desde a antiguidade, a exemplo do Homem Vitruviano de Leonardo da Vince. A existência das proporções Divinas ou proporções áureas em crânios esqueletizados é um campo de estudo que apresenta um potencial colaborador para as áreas de sobreposição de imagens, reconstrução facial e identificação forense. O objetivo deste trabalho é estabelecer parâmetros antropométricos regionais baseados nas proporções áureas, instituindo coeficientes e índices faciais específicos para a amostra composta por sessenta crânios esqueletizados (n=60), oriundos do Instituto Médico Legal da Bahia. As mensurações foram realizadas diretamente sobre o crânio utilizando paquímetros digitais. Foram selecionadas 14 medidas craniométricas diferentes e a partir delas foram calculados 06 relações horizontais, 04 verticais e 06 índices faciais. Os dados foram tabulados e descritos por valores de média e desvio padrão, utilizou-se o intervalo de confiança da média (p<0,05). A proporção áurea (1,618) não foi encontrada na população examinada, entretanto foi possível estabelecer índices faciais e coeficientes próprios para as medidas, construindo parâmetros antropométricos regionais baseados nas proporções estudadas. A proporção altura da cabeça em relação a altura da face (V-GN/N-GN) igual a 1,69 foi a variável que mais se aproximou do número de ouro e a relação entre a altura nasal ser igual a metade da altura da face (N-SN=N-GN/2) como valor de 0,92 foi a variável que mais se aproximou entre as igualdades. Admiti-se que a partir de uma medida é possível alcançar a outra utilizando os índices faciais e as retas de regressão calculadas. Este estudo fornece indicadores úteis, pois busca estabelecer proporções nos crânios que são reproduzíveis na face, contribuindo para o aperfeiçoamento das técnicas auxiliares de Reconstrução Facial e Sobreposição de Imagens em estudos futuros. / The proportions of the human face and body have been studied since, at least, Leonardo da Vinci and his Vitruvian Man. The existence of divine or Golden Ratios in human skulls may collaborate with Image Superposition, Facial Reconstruction and Forensics Identification. The goal of this study is establishing regional anthropometrics parameters based on golden ratios, using specifics facial coefficients and indexes for sixty skulls (n=60), obtained from Institute of Legal Medicine of Bahia, BRA. The measurements were done directly over the skull using a digital pachymeter. Fourteen different measurements were selected and, from them, 06 horizontals and 04 verticals relations and 06 facial indexes were calculated. The data was charted and described by average and standard deviation. An average confidence interval was used (p<0,05).The golden ratio (1,618) has not been found among the population analyzed, however was possible establishing facial indexes and coefficients for the measurements, building regional anthropometric parameters based on the studied proportions. The proportion height of the head related to the height of the face (V-GN/N-GN) equal to 1,69 was the closest variable to the golden number and the relation between nasal height being equal to half a height of the face (N-SN=N-GN/2) as 0,92 was the closest variable between equalities. Admitting that from one measurement to another can be achieved using the facial indexes facial and linear regression calculated. This study provides useful indicators since tries to establish proportions on the skull that can be reproducible on the face, contributing for the improvement of auxiliary techniques of Facial Reconstruction and Images Superposition in future studies.

Mitochondrial DNA (mtDNA) has played a major role in human population studies over the past decade due to its maternal inheritance and negligible recombination (Macaulay, 1999). The mtDNA control region has been the focus of these studies due to the highly polymorphic nature of this non-coding region. Forensic scientists also use mtDNA to help determine the identity of missing individuals when nuclear DNA is not present. However, when skeletal remains are unclaimed, identification becomes near impossible. Therefore, mtDNA can play a valuable role in identification in terms of population affiliation, especially in conjunction with morphological analysis. The goals of this research were two-fold: 1) to determine population affiliation of unknown skeletal samples using phylogenetics and 2) to find a method of extraction that leaves a majority of the remains intact. This research depended on the donation of samples from sixteen skeletal remains from the Hillsborough County Medical Examiners office. Mitochondrial DNA from ten of these cases were extracted, amplified, and sequenced in order to determine population affiliation via phylogenetic analysis of hypervariable region I (HVR I). These sequences were aligned and compared to that of sequences in a pre-existing mtDNA control region database (Handt, 1998). The crania of the skeletal remains were measured and subsequently analyzed by the forensic anthropology program FORDISC 2.0 to morphologically determine population affiliation. A secondary morphological analysis included input of the measurements into SPSS, a statistical program package, as a separate discriminant function assessment. This analysis was dependent on a database of craniometrics from known individuals (Jantz, and Moore-Jansen, 2000). The results were compared to those of FORDISC 2.0 as well as that of the phylogenetic tree constructed using molecular techniques. This study will determine whether phylogenetic analysis is a legitimate way to determine population affiliation of unknown individuals, thereby benefiting future forensic studies.

In ancestry estimation of South African individuals, non-metric morphological trait assessment has not proven useful and previous results in FORDISC 3.0 leave room for improvement. The accuracy rates of software programs FORDISC 3.1 and 3D-ID were compared for ancestry estimation based on cranial data of black and white South Africans using discriminant function analysis. Cranial landmarks were digitized using a Microscribe G2 for geometric morphometric analysis in 3D-ID, and traditional craniometric measurements for use in FORDISC were calculated using the data collection software 3Skull. Data was collected from a total of 385 individuals (186 black and 199 white crania) from the Pretoria Bone Collection. Overall accuracy rates of 75.6% using FORDISC 3.1 and 63.1% using 3D-ID were obtained for black and white South Africans. Incorrect estimates were more often due to misclassifications of sex rather than ancestry, reflecting the decreased amount of sexual dimorphism in South African populations when compared against American populations, discussed previously. Black South Africans were more often classified correctly in FORDISC 3.1, and white South Africans were more often classified correctly in 3D-ID. Low sample size in comparative databases and broad ancestral differences between South Africans and the proxy populations used (American, European, and African) likely explain the low accuracy rates. The use of FORDISC and 3D-ID in conjunction can help South African anthropologists in estimating ancestry and ensuring correct classifications.

Studies in human cranial variation are extensive and widely discussed. While skeletal biologists continue to focus on questions of biological distance and population history, group-specific knowledge is being increasingly used for human identification in medico-legal contexts. The importance of this research has been often overshadowed by both philosophic and methodological concerns. Many analyses have been constrained in their scope by the limited availability of representative samples and readily criticized for adopting statistical techniques that require user-guidance and a priori information. A multi-part project is presented here that implements model-based clustering as an alternative approach for population studies using craniometric traits. This project also introduces the use of forced-directed graphing and mixture-based supervised classification methods as statistically robust and practically useful techniques. This project considers three well-documented craniometric sources, whose samples collectively permit large-scale analyses and tests of population structure at a variety of partitions and for different goals. The craniofacial measurements drawn from the world-wide data sets collected by Howells and Hanihara permit rigorous tests for group differences and cryptic population structure. The inclusion of modern American samples from the Forensic Anthropology Data Bank allows for investigations into the importance of biosocial race and biogeographic ancestry in forensic anthropology. Demographic information from the United States Census Bureau is used to contextualize these samples within the range of the racial diversity represented in the American population-at-large. This project's findings support the presence of population structure, the utility of finite mixture methods to questions of biological classification, and the validity of supervised discrimination methods as reliable tools. They also attest to the importance of context for producing the most useful information on identity and affinity. These results suggest that a meaningful relationship between statistically inferred clusters and predefined groups does exist and that population-informative differences in cranial morphology can be detected with measured degrees of statistical certainty, even when true memberships are unknown. They imply, in turn, that the estimation of biogeographic ancestry and the identification of biosocial race in forensic anthropology can provide useful information for modern American casework that can be evidenced by scientific methods.

Este trabalho desenvolve um modelo para análise de variáveis craniométricas que utiliza as Redes Neurais Artificiais Paraconsistentes, assentadas na Lógica Paraconsistente Anotada de dois valores. Tal lógica possui a capacidade de mensurar incerteza, inconsistência e paracompleteza. A Lógica Paraconsitente vem sendo empregada em diversas aplicações sujeitas a estas situações, constituindo nova ferramenta matemática em Inteligência Artificial. O trabalho tem como principal objetivo melhorar o diagnóstico cefalométrico. O modelo desenvolvido recebe as medidas das variáveis craniométricas de um determinado paciente e as compara com as médias das variáveis craniométricas normais de uma amostra da população brasileira. Esta amostra é composta de crianças e adolescentes de ambos os sexos, na faixa etária de 6 a 18 anos, utilizadas neste trabalho como valores de referência de normalidade. A análise cefalométrica aqui proposta consiste em quantificar discrepâncias esqueletais e dentárias sob a Lógica Paraconsistente. O uso das Redes Neurais Artificiais Paraconsistentes permite agregar ao méto do um fator de incerteza, respeitando o diagnóstico ortodôntico tradicional, e ao mesmo tempo, contextualiza diferentes regiões craniofaciais. O resultado da análise consiste dos graus de discrepância esqueletal, anteroposterior e vertical, e graus de discrepância dentárias, relativas aos incisivos inferiores e superiores. Variáveis craniométricas de 120 pacientes foram processadas pelo modelo proposto e avaliadas por três especialistas em Ortodontia. De acordo com o índice Kappa, houve desde concordância satisfatória até concordância quase perfeita entre o modelo e os especialistas, de acordo com as variáveis consideradas. As opiniões inter-especialista são substancialmente similares às comparações entre os especialistas e o modelo apresentado, o que reflete o potencial do modelo como um sistema especialista. A utilização de técnicas de Inteligência Artificial através da Lógica Paraconsistente, permitiu significante melhora na análise cefalométrica proposta. O modelo apresentado pode ser adaptado a outras amostras ou populações, com a adaptação dos valores de referência iniciais de normalidade. / This work shows the development of an unequal craniometric analysis model, which uses Paraconsistent Neural Network, based upon Paraconsistent Logic with two values. Such logical approach has the capability to handle concepts as uncertainness, inconsistency and paracompleteness. It has been used on diverse applications which present such features, constituting a new mathematical tool in Artificial Intelligence. The presented methodology had as main goal to booster diagnosis in Orthodontics. The developed model processes craniometric measures of a specific person, and compares to the expected means drawn from a Brazilian sample, comprised of children and adolescent individuals, ranging from 6 to 18 year-old, of both genders. The current cephalometric analysis, developed under the approach of Paraconsistent Logic, quantifies skeletal and dental discrepancies. The use of Paraconsistent Neural Network allows aggregating a factor of vagueness, respecting the limits of traditional orthodontic classification. At the same time, it contextualizes variables of different craniofacial regions. The results of the analysis are expressed through degrees of skeletal discrepancies, in the anteroposterior and vertical dimensions, and degrees of dental discrepancies, for the upper and lower incisors. Cephalometric va lues of a sample of 120 patients were processed by the paraconsistent model and analyzed by three specialists in Orthodontics. According to Kappa index, the agreement between the model and the specialists ranged from moderate to almost perfect, according to the variables considered. The inter-observer opinions were substantially similar to the mathematical model, which reflects the potential of the model as a specialist system. The use of Artificial Intelligence methods throughout Paraconsistent Logics, allowed significant improvement in cephalometric assessment. The presented model can be applied in different samples or populations, with adaptation of the degrees of normality as initial references.

Este trabalho desenvolve um modelo para análise de variáveis craniométricas que utiliza as Redes Neurais Artificiais Paraconsistentes, assentadas na Lógica Paraconsistente Anotada de dois valores. Tal lógica possui a capacidade de mensurar incerteza, inconsistência e paracompleteza. A Lógica Paraconsitente vem sendo empregada em diversas aplicações sujeitas a estas situações, constituindo nova ferramenta matemática em Inteligência Artificial. O trabalho tem como principal objetivo melhorar o diagnóstico cefalométrico. O modelo desenvolvido recebe as medidas das variáveis craniométricas de um determinado paciente e as compara com as médias das variáveis craniométricas normais de uma amostra da população brasileira. Esta amostra é composta de crianças e adolescentes de ambos os sexos, na faixa etária de 6 a 18 anos, utilizadas neste trabalho como valores de referência de normalidade. A análise cefalométrica aqui proposta consiste em quantificar discrepâncias esqueletais e dentárias sob a Lógica Paraconsistente. O uso das Redes Neurais Artificiais Paraconsistentes permite agregar ao méto do um fator de incerteza, respeitando o diagnóstico ortodôntico tradicional, e ao mesmo tempo, contextualiza diferentes regiões craniofaciais. O resultado da análise consiste dos graus de discrepância esqueletal, anteroposterior e vertical, e graus de discrepância dentárias, relativas aos incisivos inferiores e superiores. Variáveis craniométricas de 120 pacientes foram processadas pelo modelo proposto e avaliadas por três especialistas em Ortodontia. De acordo com o índice Kappa, houve desde concordância satisfatória até concordância quase perfeita entre o modelo e os especialistas, de acordo com as variáveis consideradas. As opiniões inter-especialista são substancialmente similares às comparações entre os especialistas e o modelo apresentado, o que reflete o potencial do modelo como um sistema especialista. A utilização de técnicas de Inteligência Artificial através da Lógica Paraconsistente, permitiu significante melhora na análise cefalométrica proposta. O modelo apresentado pode ser adaptado a outras amostras ou populações, com a adaptação dos valores de referência iniciais de normalidade. / This work shows the development of an unequal craniometric analysis model, which uses Paraconsistent Neural Network, based upon Paraconsistent Logic with two values. Such logical approach has the capability to handle concepts as uncertainness, inconsistency and paracompleteness. It has been used on diverse applications which present such features, constituting a new mathematical tool in Artificial Intelligence. The presented methodology had as main goal to booster diagnosis in Orthodontics. The developed model processes craniometric measures of a specific person, and compares to the expected means drawn from a Brazilian sample, comprised of children and adolescent individuals, ranging from 6 to 18 year-old, of both genders. The current cephalometric analysis, developed under the approach of Paraconsistent Logic, quantifies skeletal and dental discrepancies. The use of Paraconsistent Neural Network allows aggregating a factor of vagueness, respecting the limits of traditional orthodontic classification. At the same time, it contextualizes variables of different craniofacial regions. The results of the analysis are expressed through degrees of skeletal discrepancies, in the anteroposterior and vertical dimensions, and degrees of dental discrepancies, for the upper and lower incisors. Cephalometric va lues of a sample of 120 patients were processed by the paraconsistent model and analyzed by three specialists in Orthodontics. According to Kappa index, the agreement between the model and the specialists ranged from moderate to almost perfect, according to the variables considered. The inter-observer opinions were substantially similar to the mathematical model, which reflects the potential of the model as a specialist system. The use of Artificial Intelligence methods throughout Paraconsistent Logics, allowed significant improvement in cephalometric assessment. The presented model can be applied in different samples or populations, with adaptation of the degrees of normality as initial references.

With both a heterogeneous population and a large number of unidentified persons in South Africa, an accurate method to estimate ancestry is needed. The purpose of this study was to evaluate variation in nasal aperture shape in black, white and coloured South Africans, using linear measures and geometric morphometrics (GM), the latter which includes both procrustes analysis (GPA) and elliptical fourier analysis (EFA). To test statistical significance among groups, discriminant function analysis (DFA) and principal component analysis (PCA) was used. A total of 310 (164 male, 145 female) crania of black, white and coloured South Africans were used. Thirteen standard landmarks, namely, glabella, nasion, nasale superior, dacryon, nasale inferius, alare, most inferior nasal border and subspinale, were digitised with a MicroScribe G2™ (Immersion: San Jose, CA). Five linear measures, nasion-dacryon angle (NDA), nasal breadth (NLB), nasal height (NLH), inter-orbital breadth (DKB) and nasion-dacryon subtense (NDS), were calculated. For EFA, photographs were taken in a frontal plane of skulls that had been positioned in the Frankfort horizontal plane on a craniophore. All classification accuracies for all groups were better than chance. Using linear measures and GPA, black South Africans classified 55-71% correctly, coloured classified 53-61% correctly and whites classified 85-95% correctly. Black and coloured South Africans demonstrated bell-shaped nasal apertures with nasal spines superior to the inferior nasal border. White South Africans had pear-shaped nasal apertures with a nasal spine inferior of the inferior nasal border. Using EFA black South Africans classified 62% correctly. While coloured South Africans only classified 39% correctly, which demonstrates high within group variability. Due to their unique historical development, large variation (heterogeneity) within the coloured group was expected. White South Africans had the highest correct classification accuracy of 85%. For all methods, misclassification rarely occurred between white and non-white (black and coloured) groups and most difficulties arose in distinguishing non-white groups from each other. High rates of misclassification was also noted between sex designations within a group, which suggests less or an absence of sexual dimorphism for these variables The distinct separation of white South Africans may reflect the mid-to late 20th century political and social separation of white and non-white groups in South Africa. Nasal aperture shape, alone, is less useful for separating groups such that all groups have relatively intermediate nasal aperture shapes; however the pinched nasal bone structure of white South Africans clearly separates them from the other groups. When using nasal bone and aperture landmarks, linear measures are as accurate as the modern geometric techniques in distinguishing groups. All methods are feasible to use in the estimation of ancestry on modern South Africans, with craniometry a sensible solution as the data can be rapidly collected, accurately analysed and compared to current reference samples. Copyright / Dissertation (MSc)--University of Pretoria, 2012. / Anatomy / unrestricted

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