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再探普尔凯特三角:在性别和血统估计中的作用

Purkait's triangle revisited: role in sex and ancestry estimation.

作者信息

Attia MennattAllah Hassan, Attia Mohamed Hassan, Farghaly Yasmin Tarek, Abulnoor Bassam Ahmed El-Sayed, Manolis Sotiris K, Purkait Ruma, Ubelaker Douglas H

机构信息

Forensic Medicine & Clinical Toxicology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt.

Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia.

出版信息

Forensic Sci Res. 2022 Feb 14;7(3):440-455. doi: 10.1080/20961790.2021.1963396. eCollection 2022.

Abstract

Identification of unknown remains recovered from marine and terrestrial locations is a significant humanitarian problem. This investigation proposes a simple method applicable to fragmentary femora for a more refined level of ancestry and/or sex estimation. To that end, we re-examined Purkait's triangle which involves three inter-landmark distances between the traction epiphyses and the articular rim of femoral head. A large sample ( = 584) from geographically diverse (Egyptian, Indian and Greek) populations was compiled. Additionally, shape ( = 3) and trigonometrically derived variables and ratios ( = 9 variables) were employed to detect any geographically-clustered morphological differences between these populations. Random forest modelling (RFM) and linear discriminant function analysis (LDA) were employed to create classification models in instances where sex was known or unknown. The sample was apportioned into training and test sets with a ratio 70/30. The classification accuracies were evaluated by means of fold cross-validation procedure. In sex estimation, RFM showed similar performance to LDA. However, RFM outperformed LDA in ancestry estimation. Ancestry estimation was satisfactory in the Indian and Egyptian samples albeit the Greek sample was problematic. The Greek samples presented greater morphological overlap with the Indian sample due to high within-group variation. Test samples were accurately assigned to their ancestral category when sex was known. Generally, higher classification accuracies in the validation sample were obtained in the sex-specific model of females than in males. Using RFM and the linear variables, the overall accuracy reached 83% which is distributed as 95%, 71% and 86% for the Egyptian, Indian and Greek females, respectively; whereas in males, the overall accuracy is 72% and is distributed as 58%, 87% and 50% for the Egyptian, Indian and Greek males, respectively. Classification accuracies were also calculated per group in the test data using the 12 derived variables. For the females, the accuracies using the medians model was comparable to the linear model whereas in males the angles model outperformed the linear model for each group but with similar overall accuracy. The classification rates of male specific ancestry were 82%, 78% and 56% for the Egyptian, Indian and Greek males, respectively. In conclusion, Purkait's triangle has potential utility in ancestry and sex estimation albeit it is not possible to separate all groups successfully with the same efficiency. Intrapopulation variation may impact the accuracy of assigned group membership in forensic contexts. Key pointsPurkait's method is a possible ancestry group indicator applicable to fragmentary femora.Random forest model surpassed linear discriminant function analysis in multi-group ancestry classification.Ancestry is more accurately assessed in females than males.The intertrochanteric distance is the most important feature in discrimination of sex whereas in ancestry it was the head to lesser trochanter distance.Sex differences override ancestry due to the tendency of misclassification into same sex but different group rather than the opposite sex of the same ancestry.

摘要

从海洋和陆地 locations 回收的未知遗骸的鉴定是一个重大的人道主义问题。本研究提出了一种简单的方法,适用于不完整的股骨,以进行更精细的血统和/或性别估计。为此,我们重新审视了 Purkait 三角形,该三角形涉及股骨近端骨骺与股骨头关节边缘之间的三个地标间距离。收集了来自不同地理区域(埃及、印度和希腊)的大量样本(n = 584)。此外,还采用了形状变量(n = 3)和三角学推导变量及比率(n = 9 个变量)来检测这些人群之间任何地理聚类的形态差异。在已知或未知性别的情况下,采用随机森林建模(RFM)和线性判别函数分析(LDA)来创建分类模型。样本按 70/30 的比例分为训练集和测试集。通过 10 折交叉验证程序评估分类准确率。在性别估计中,RFM 与 LDA 表现相似。然而,在血统估计方面,RFM 优于 LDA。印度和埃及样本的血统估计结果令人满意,尽管希腊样本存在问题。由于组内变异较大,希腊样本与印度样本呈现出更大的形态重叠。当已知性别时,测试样本能准确归为其所属的血统类别。一般来说,验证样本中女性特定性别的模型分类准确率高于男性。使用 RFM 和线性变量,总体准确率达到 83%,埃及、印度和希腊女性的准确率分别为 95%、71%和 86%;而男性的总体准确率为 72%,埃及、印度和希腊男性的准确率分别为 58%、87%和 50%。还使用 12 个推导变量在测试数据中按组计算分类准确率。对于女性,中位数模型的准确率与线性模型相当,而对于男性,角度模型在每组中均优于线性模型,但总体准确率相似。埃及、印度和希腊男性特定血统的分类率分别为 82%、78%和 56%。总之,Purkait 三角形在血统和性别估计中具有潜在用途,尽管无法以相同效率成功区分所有群体。群体内变异可能会影响法医背景下指定群体成员身份的准确性。要点Purkait 方法是一种适用于不完整股骨的可能的血统群体指标。随机森林模型在多群体血统分类中超过了线性判别函数分析。女性的血统评估比男性更准确。转子间距离是性别判别中最重要的特征,而在血统判别中是股骨头至小转子的距离。由于错误分类倾向于同性但不同群体而非同一血统的异性,性别差异超过了血统差异。

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