Search

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (Crecon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + Crecon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Taibah University Medical Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

الملخص: أهداف البحث: قام الباحثون بفحص العديد من خصائص الأسنان لتحديد الاختلافات في شكل الأسنان بين الذكور والإناث في مجموعات سكانية مختلفة. ومع ذلك، لم يتم إجراء أي بحث للتأكد من إزدواج الشكل الجنسي وتطوير نموذج للتنبؤ بالجنس باستخدام نمط الأخاديد وعدد الحدبات ونمط الإطباق في أي مجموعة. وبذلك توصلت هذه الدراسة إلى الفروق الجنسية والقدرة على التنبؤ بالجنس بناء على هذه الخصائص للأسنان العلوية في المجتمع الباكستاني. طرق البحث: تم اختيار 130 قالبا سنيا (65 ذكر و65 أنثى). تم التقاط صور رقمية للسطح الإطباقي للضاحك الأول والثاني والطاحن الأول العلوي باستخدام كاميرا رقمية. تم تسجيل عدد الحدبات، ونمط الأخاديد، والنمط الإطباقي لكل نوع من الأسنان. تم تحليل البيانات باستخدام اختبار ''مربع كاي'' وتحليل الانحدار اللوجستي. النتائج: أظهر نمط الأخاديد والنمط الإطباقي للضاحك الأول والطاحن الأول العلوي تباينا جنسيا كبيرا. كما أظهر عدد الحدبات في الطاحن الأول العلوي فرقا كبيرا ذا دلالة إحصائية بين الذكور والإناث. بلغت دقة التنبؤ بالجنس للعينات التدريبية 76.7%، بينما كانت 70% للعينات الاختبارية. الاستنتاجات: وجد تباين جنسي كبير في نمط الأخاديد والنمط الإطباقي للضاحك الأول والطاحن الأول العلوي، وكذلك في عدد الحدبات للطاحن الأول العلوي. أظهر نموذج التنبؤ دقة جيدة بنسبة 76.7%، وبالتالي يمكن استخدامه للتنبؤ بالجنس في المجتمع الباكستاني. Abstract: Objective: Researchers have examined several dental characteristics to identify differences in tooth morphology between males and females in various populations. Nevertheless, no research has been undertaken to ascertain sexual dimorphism and develop a sex prediction model by using the groove pattern, cusp number and occlusal pattern in any population group. Therefore, this study accessed the sexual differences and the ability to predict sex according to these characteristics of maxillary teeth in the Pakistani community. Method: A total of 130 dental casts were selected (65 each from males and females). Digital images of the occlusal surface of the maxillary first premolar, second premolar and first molar were captured with a Canon Powershot A2200 14.1 MP digital camera with 4× optical zoom. Cusp number, groove pattern and occlusal pattern were recorded for each tooth type. Data were analysed with chi-square tests and logistic regression analysis. Results: The groove pattern and occlusal pattern of the maxillary first premolar and first molar showed significant sexual dimorphism (p

Objective: Sex determination has been found interesting in forensic handwriting examinations and has been researched by scientists. The inclusion of the sex parameter as a supporting element in the examination of forensic handwriting while deciding belonging will increase the reliability of the results. In addition, it will help reduce the number of people to be investigated for a large group of suspects, both men and women. In this study, it was aimed to investigate the contribution of the ascender and descender parts of the letters to sex prediction by measuring them. Methods: In line with this purpose, handwriting samples were collected from 50 female and 50 male participants by having them write 11 sentences containing the letters “b, d, f, g, h, k, t, y, p” at initial, medial, and end positions. The ascender and descender parts of these letters were measured in millimeters. Logistics, k-nearest neighbor (KNN), support vector machine (SVM) and artificial neural network (ANN) were selected and applied to these data. Results: The ascender and descender parts of these letters were measured in millimeters and statistically significant differences were found between male and female participants. The ascender parts of the “b, d, h, k, t” were determined to be statistically significantly longer in males. Accuracy rates are 0.65, 0.60, 0.71 and 0.82 for Logistics, KNN, SVM and ANN, respectively. Conclusion: In our opinion, this result is promising. If the studies on this subject are increased, higher success rates can be achieved, and more contributions can be made to forensic handwriting examination.

Copyright of Bulletin of Legal Medicine / Adli Tıp Bülteni is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The attribution of biological sex and age are important aspects in forensic medicine investigations in establishing the identity of unknown skeletal remains. Where remains are incomplete or have been compromised by damage or fragmentation, this can affect the accuracy to which sex and age can be determined and require the development of other tecniques using different bone elements or isolateed bone elements. The objective of the paper was to conduct a descriptive and inferential analysis on certain parameters studied on the lumbar spine with implication in estimating sex and age. The present study used 149 images of magnetic resonance of the vertebral lumbar column, on which the authors perfomed a large-scale analysis of three parameters measured on the RM images from a modern Romanian population. The results of the study found that mostly all variables analysed are involved in evaluation of sexual dismorphism. Regarding age prediction, in this study we conclude that some analyzed parameters are important in providing significant age differences. The present work is a novelty in the field and brings more originality, as it took into account all 5 lumbar vertebrae. The lumbar vertebrae can reliably providing an additional element to the growing list of postcranial skeletal elements that can aid in developing the biological profile of unidentified human remains. However, we need further studies with a larger number of images and to derive population-specific discriminant and regression functions. [ABSTRACT FROM AUTHOR]

Sex estimation from skeletal remains is one of the crucial issues in forensic anthropology. Long bones can be a valid alternative to skeletal remains for sex estimation when more dimorphic bones are absent or degraded, preventing any estimation from the first intention methods. The purpose of this study was to generate and compare classification models for sex estimation based on combined measurement of long bones using machine learning classifiers. Eighteen measurements from four long bones (radius, humerus, femur, and tibia) were taken from a total of 2141 individuals. Five machine learning methods were employed to predict the sex: a linear discriminant analysis (LDA), penalized logistic regression (PLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The different classification algorithms using all bones generated highly accuracy models with cross-validation, ranging from 90 to 92% on the validation sample. The classification with isolated bones ranked between 83.3 and 90.3% on the validation sample. In both cases, random forest stands out with the highest accuracy and seems to be the best model for our investigation. This study upholds the value of combined long bones for sex estimation and provides models that can be applied with high accuracy to different populations. [ABSTRACT FROM AUTHOR]

Background: The aim of this study is to predict sex with machine learning (ML) algorithms by making morphometric measurements on radiological images of the first and fifth metatarsal and phalanx bones. Materials and methods: In this study, radiologic images of 263 individuals (135 female, 128 male) between the ages of 27 and 60 were analysed retrospectively. The images in digital imaging and communications in medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer programme. Length and width measurements of the first and fifth metatarsal and foot phalanx bones were performed on the transferred images. In addition, the ratios of the total length of the first proximal and distal phalanx and length of the first metatarsal and total length of fifth proximal, middle, and distal phalanx and maximum length of fifth metatarsal were calculated. Results: As a result of machine learning algorithms, highest accuracy, specificity, sensitivity, and Matthews correlation coefficient values were found as 0.85, 0.86, 0.85, and 0.71, respectively with decision tree algorithm. It was found that accuracy rates of other algorithms varied between 0.74 and 0.83. Conclusions: As a result of our study, it was found that sex estimation was made with high accuracy rate by using machine learning algorithms on X-ray images of the first and fifth metatarsal and foot phalanx. We think that in cases when pelvis, cranium and long bones are harmed and examination is difficult, bones of the first and fifth metatarsal and foot phalanx can be used for sex estimation. [ABSTRACT FROM AUTHOR]

Sex prediction from bone measurements that display sexual dimorphism is one of the most important aspects of forensic anthropology. Some bones like the skull and pelvis display distinct morphological traits that are based on shape. These morphological traits which are sexually dimorphic across different population groups have been shown to provide an acceptably high degree of accuracy in the prediction of sex. A sample of 100 patella of Mixed Ancestry South Africans (MASA) was collected from the Dart collection. Six parameters: maximum height (maxh), maximum breadth (maxw), maximum thickness (maxt), the height of articular facet (haf), lateral articular facet breadth (lafb), and medial articular facet breath (mafb) were used in this study. Stepwise and direct discriminant function analyses were performed for measurements that exhibited significant differences between male and female mean measurements, and the "leave-one-out" approach was used for validation. Moreover, we have used eight classical machine learning techniques along with feature ranking techniques to identify the best feature combinations for sex prediction. A stacking machine learning technique was trained and validated to classify the sex of the subject. Here, we have used the top performing three ML classifiers as base learners and the predictions of these models were used as inputs to different machine learning classifiers as meta learners to make the final decision. The measurements of the patella of South Africans are sexually dimorphic and this observation is consistent with previous studies on the patella of different countries. The range of average accuracies obtained for pooled multivariate discriminant function equations is 81.9–84.2%, while the stacking ML technique provides 90.8% accuracy which compares well with those presented for previous studies in other parts of the world. In conclusion, the models proposed in this study from measurements of the patella of different population groups in South Africa are useful resent with reasonably high average accuracies. [ABSTRACT FROM AUTHOR]

Purpose: The uppermost segment of the cervical vertebra or atlas (C1) is a critically important anatomical structure, housing the medulla oblongata and containing the grooves for the C1 spinal nerve and the vertebral vessels. Variations of the C1 vertebra can affect upper spine stability, and morphometric parameters have been reported to differ by population. However, there are few data regarding these parameters in Thais. The use of this bone to predict sex and age has never been reported. Methods: This study aimed to examine C1 morphometry and determine its ability to predict sex. Twelve diameter parameters were taken from the C1 vertebrae of identified skeletons (n = 104, males [n, 54], females [n, 50]). Correlation analysis was also performed for sex and age, which were predicted using machine learning algorithms. Results: The results showed that 8 of the 12 measured parameters were significantly longer in the male atlas (p < 0.05), while the remaining 4 (distance between both medial-most edges of the transverse foramen, transverse dimension of the superior articular surface, frontal plane passing through the canal's midpoint, and anteroposterior dimension of the inferior articular surface) did not differ significantly by sex. There was no statistically significant difference in these parameters on the lateral side. The decision stump classifier was trained on C1 parameters, and the resulting model could predict sex with 82.6% accuracy (root mean square error = 0.38). Conclusion: Assertation of the morphometric parameters of the atlas is important for preoperative assessment, especially for the treatment of atlas dislocation. Our findings also highlighted the potential use of atlas measurements for sex prediction. [ABSTRACT FROM AUTHOR]

A number of criteria for the assessment of biological sex, which are applied to living or recently deceased individuals, have been developed, such as genetic, chromosomal, gonadal, hormonal, and phenotypic criteria. Features of a metric and descriptive nature are used to assess the sex of skeletal materials. The diagnostic features of the skull are concentrated in the craniofacial region and around the eye sockets. The mandible is a diagnostically important part of the skull, on which a complex of features is visible. These features develop up to the third decade of life. The goal of the research was to assess the suitability of the parameters of the preangular notch, in other words, the length, height, and surface area for sex prediction applied to skeletal materials. The study included computed tomography images of the masticatory system of 194 patients, consisting of 83 females and 111 males, aged from 16 to 93 years. The three straight lines which correspond to the sides of the triangle representing the notch, in addition to its height, were determined and measured digitally. The receiver operating characteristic method was used to assess the usefulness of the studied features for the purposes of sex prediction. The sensitivity of the test ranged from 51.4% to 67% for the parameters of the preangular notch on the right‐hand side, and from 44.4% to 80.2% on the left‐hand side. The most reliable predictive models were obtained for two features (shown in the graphical abstract). However, when taking into account, the specificity and sensitivity of the tests presented here, only the length of side AB (a basal length of the notch) can be regarded as a feature that supports the assessment of sex on the basis of other diagnostic features of the mandible. The size and shape of the preangular notch should be treated as auxiliary features in the assessment of sex. For this reason, it is recommended that they should be applied simultaneously to the set of features described in the section on anthropological standards. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The hard palate participates in the construction of the oral and nasal cavities and represents the bony barrier be- tween them. Morphological differences of the hard palate are important for forensic medicine, anthropology, anatomy, as well as scientific branches dealing with the study of evolutionary development, populations differences. The aim of this study was to determine sexual dimorphism of hard palate on three-dimensional (3D) models of human skulls using geometric morphometry. MATERIALS AND METHODS: The research was conducted on 3D models of 209 human skulls from Bosnian population (139 male, 70 female). On the obtained 3D models, we marked landmarks on the palate using Landmark editor program. Using MorphoJ program we analyzed sex differences of shape and size on hard palate. RESULTS: The principal component analysis showed that the first two components (PC1 and PC2) described 55.503% of the total morphological variability of the hard palate. The results of the discriminant analysis showed predictive power for male with 66.91% accuracy and for female with 58.57% accuracy based on the shape and size of the hard palate. The influence of size of the hard palate on its shape was statistically significant (p<0.0001). The results of discriminant analysis based on shape of hard palate showed predictive power for male with 68.34% accuracy and for female with 64.29% accuracy. CONCLUSIONS: Sex differences of hard palate are statistically significant and can be used for sex determination in skeletal remains. The percentage of accuracy for determining sex based on the hard palate was higher for men in this study. [ABSTRACT FROM AUTHOR]

Abstract Background Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. Results Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p

Background: Deep convolutional neural networks (DCNNs) for diagnosis of disease on chest radiographs (CXR) have been shown to be biased against males or females if the datasets used to train them have unbalanced sex representation. Prior work has suggested that DCNNs can predict sex on CXR, which could aid forensic evaluations, but also be a source of bias.Objective: To (1) evaluate the performance of DCNNs for predicting sex across different datasets and architectures and (2) evaluate visual biomarkers used by DCNNs to predict sex on CXRs.Materials and Methods: Chest radiographs were obtained from the Stanford CheXPert and NIH Chest XRay14 datasets which comprised of 224,316 and 112,120 CXRs, respectively. To control for dataset size and class imbalance, random undersampling was used to reduce each dataset to 97,560 images that were balanced for sex. Each dataset was randomly split into training (70%), validation (10%), and test (20%) sets. Four DCNN architectures pre-trained on ImageNet were used for transfer learning. DCNNs were externally validated using a test set from the opposing dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to generate heatmaps visualizing the regions contributing to the DCNN's prediction.Results: On the internal test set, DCNNs achieved AUROCs ranging from 0.98 to 0.99. On external validation, the models reached peak cross-dataset performance of 0.94 for the VGG19-Stanford model and 0.95 for the InceptionV3-NIH model. Heatmaps highlighted similar regions of attention between model architectures and datasets, localizing to the mediastinal and upper rib regions, as well as to the lower chest/diaphragmatic regions.Conclusion: DCNNs trained on two large CXR datasets accurately predicted sex on internal and external test data with similar heatmap localizations across DCNN architectures and datasets. These findings support the notion that DCNNs can leverage imaging biomarkers to predict sex and potentially confound the accurate prediction of disease on CXRs and contribute to biased models. On the other hand, these DCNNs can be beneficial to emergency radiologists for forensic evaluations and identifying patient sex for patients whose identities are unknown, such as in acute trauma. [ABSTRACT FROM AUTHOR]

Copyright of Revista Española de Documentación Científica is the property of Consejo Superior de Investigaciones Cientificas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explored the hidden layer activations of previously published Deep Neural Networks (DNNs) that detect age-related diastolic function changes through echocardiographic parameters. The study also aimed to decode the prediction mechanism to understand if biological age and sex were latent factors in the DNN model. We performed a secondary analysis of a previously published DNN model trained using data from 1009 patients (62 ± 17 years, 57 % females) to predict risk phenogroups from nine echocardiographic parameters. It was tested on an internal (n = 243, mean age = 62 ± 17 years, ∼57 % females) and an external (n = 5596, mean age = 76 ± 5 years, ∼57 % females) validation cohort. To predict biological age and sex, linear regression and classification models were developed using hidden layer activations from the DNN. Model performance was evaluated using Pearson's correlation for regression; accuracy and area under the curve (AUC) metrics for classification. On assessing the hidden layer activations, we observed that the model captured biological age accurately in both younger and older populations, especially in the low-risk phenogroup with strong correlations for the total population (0.94, p < 0.001), males (0.90, p < 0.001), and females (0.94, p < 0.001). In the high-risk phenogroup, the correlations were lower (0.31, p = 0.274) for the entire population, (0.76, p = 0.003) for males, and (0.11, p = 0.723) for females. Sex was predicted as a latent variable in the internal (accuracy 58.02 %) and external (accuracy 52.2 %) validation cohorts, with an AUC of 0.65 for both groups. The study indicates that DNN's latent space retains an age-dependent link with diastolic functional parameters, providing an age-independent solution for predicting diastolic dysfunction. Network dissection can yield a further understanding of the learned information in DNNs, providing novel pathophysiological insights for clinicians. [ABSTRACT FROM AUTHOR]

Background: Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. Results: Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p<0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first principal components of the DNA methylation data of sex-associated probes mapped on sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. Conclusions: This proposed sex classifier not only can be used for sex predictions but also applied to identify samples with sex chromosome aneuploidy, and it is freely and easily accessible by calling the 'estimateSex' function from the newest wateRmelon Bioconductor package (https://github.com/schalkwyk/wateRmelon). [ABSTRACT FROM AUTHOR]

Estimation of sex is an essential parameter which helps in establishing the biological profile of the deceased in forensic and archaeological examinations. There are two types of methods useful in the estimation of sex –morphological and morphometric. While morphological analysis is subjective and involves visual inspection of the dimorphic traits, morphometric analysis involves statistical analysis and comparison of the measurements to develop a probabilistic estimation of sex. Most studies estimate sex using Discriminant Function Analysis (DFA) that has many restrictive conditions, however, the logistic regression does not have these restrictions and is suitable for a two class problem like estimation of sex.In the present investigation, an attempt has been made to estimate sex by developing logistic regression equations using a cross-sectional sample of 344 young adults (172 females and 172 males). Eight anthropometric measurements were taken on the participants with standard procedures. Hand breadth shows the highest sexual dimorphism and is the best classifier of sex, followed by foot length and hand length. A combined regression model using these three variables has also been proposed that can correctly classify sex with an accuracy of 90.1 % and 91.3 % for males and females, respectively. The likelihood ratio is calculated from the normal probability density curve constructed from the mean and standard deviation for various predictors obtained for the population. It has been concluded that the likelihood ratios corroborate the classification well and the likelihood ratio with its threshold at '1′ can be used as a classifier when there are only two classes. The performance of the likelihood ratio classifier in the present study has been found to be equal to the logistic regression classifier.

Teeth are the hardest tissue in the body. Mandibular canines are important tooth in the human body which shows significant sexual dimorphism. Mandibular canine index can be used for gender identification during mass disasters and crime scenes. The purpose of this study was to investigate the accuracy with which gender can be differentiated in Malwa population by using the Mandibular Canine Index and sexual dimorphism.Materials and Method: The study sample includes 300 Mandibular casts of people residing in Malwa. It included 137 males and 163 female participants. Mesio distal diameter of mandibular canines were measured from mesial contact point to distal contact point of right and left mandibular canines. Inter canine distance was measured between tips of bothcanines. These were used for deriving MCI and determining sexual dimorphism exhibited by mandibular canines.Results: The mesiodistal width of mandibular canine were larger in males than in females. The mean difference of MCI between males and females were more on the left canine, thus making it highly significant (P Value<0.001). The standard mandibular canine index for Malwa population was obtained as 0.262. Sexual dimorphism of right mandibular canine was estimated as 4.30% and for left mandibular canine was 5.58%. Sexual dimorphism of mandibular canine is more on left side. The overall percentage accuracy of sex prediction using standard mandibular canine was found to be 65.52%. Conclusion: Tooth remains obtained from crime scenes or disasters can be used for gender identification as this method is very simple, accurate and useful. [ABSTRACT FROM AUTHOR]

I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE and 9–11th Centuries CE) in central Italy. A generalized low rank model (GLRM) was used to impute missing data and Area under the Curve—Receiver Operating Characteristic (AUC-ROC) with 20-fold stratified cross-validation was used to evaluate predictive performance of eight machine learning algorithms on different subsets of the data. Additional perspectives such as this one show strong potential for sex prediction in bioarchaeological and forensic anthropological contexts. Furthermore, GLRMs have the potential to handle missing data in ways previously unexplored in the discipline. Although results of this study look promising (highest AUC-ROC = 0.9722 for predicting binary male/female sex), the main limitation is that the sexes of the individuals included were not known but were estimated using standard macroscopic bioarchaeological methods. However, future research should apply this machine learning approach to known-sex reference samples in order to better understand its value, along with the more general contributions that machine learning can make to the reconstruction of past human lifeways.

Background: Analysis of the bones and bone fragments of the cranium may be a useful tool for sex diagnosis in the identification of human remains which have been exposed to adverse conditions. The object of the present study was to evaluate sex prediction through metric and non-metric analysis of the hard palate (HP) and the pyriform aperture (PA), using macerated skulls of adult individuals.Materials and Methods: We analysed 312 dry skulls of adult individuals of both sexes, studying the metric and non-metric characteristics of the HP and PA. The accuracy, sensitivity, specificity and positive and negative predictive values were evaluated. A binary logistic regression and a linear regression were performed. The receiver operating characteristic curve was constructed to analyse the perfor- mance of sex diagnosis. Measurements of the HP and the PA were analysed by ANOVA and Tukey's test. The SPSS v. 20.0 software was used, with a significance threshold of 5%.Results: The shape of the PA presented 61.9% accuracy, 54.4% sensitivity and 65.7% specificity. The shape of the HP presented 51.5% accuracy, 65.6% sen- sitivity and 44.7% specificity. Only the height of the PA functioned as a good predictor of sex.Conclusions: The height of the PA produced good diagnostic performance (area under curve = 0.764). The height of the PA was the most reliable indicator for sex prediction, and could be used by forensic scientists to identify sex. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine sexual dimorphism as well as to predict sex using facial linear dimensions and angles among Hausas of Kano state Nigeria. A total of 283 subjects comprising 147 males and 136 females age range 18–25 years participated. Photographs methods were used to capture the face. Independent sample t-test was used to test for sex differences in the variables. Binary logistic regression was applied to obtain a predicting equation (BLR model) for sex. The predicted probabilities of BLR were analyzed using receiver operating characteristic curve. The results showed that all the facial linear dimensions showed significance sexual dimorphism except interocular distance, upper facial width, philtrum length, lower vermilion width, left and right orbital width. With regards to sex prediction, upper facial height was the single best predictor of sex with an accuracy of 76.2% and 24–33% contribution to the prediction. However, the percentage accuracy increased to 91% when six variables were pooled together in the equations. For facial angles, only nasion and aperture modified angle did not show significant gender differences. However, in the variables with significant sexual dimorphism only nasomental angle showed a significant level of sex prediction with an accuracy of 70.3%. In conclusion, sex discrimination using facial linear dimensions and angles was well established in this study. The sex of an individual of Hausa ethnic group can be determined using facial linear dimensions. Dispite sexual dimorphsm shown by facial angles, only nasomental angle was good discriminator of sex.

Short Tandem Repeat (STR) genotyping is currently the primary DNA-based method for human identification, however it can have limited success when applied to degraded human remains. Massively parallel sequencing (MPS) provides new opportunities to obtain genetic data for hundreds of loci in a single assay with higher success from degraded samples. However, due to the extra requirement for specialised equipment, expertise and resources, routine use of MPS may not be feasible or necessary for many forensic cases. Here we describe the development of a mini-multiplex SNaPshot screening tool (Miniplex) for human samples which allows the qualitative comparison of short mitochondrial and nuclear DNA targets, as well as the interrogation of biogeographic ancestry, lineage, and phenotype single nucleotide polymorphisms (SNPs). This tool is useful to triage samples based on sample quality prior to downstream identification workflows and provides broad biological profile data for intelligence purposes. [ABSTRACT FROM AUTHOR]

The prime aim of the study was to establish sex prediction assessment via mandibular canine index (MCI) and logistic regression in Pakistani population thorough a digital model study. The selected sample consisted of 128 subjects with the ages ranged from 18 to 24 years. The mesiodistal and buccolingual tooth size were measured via scanned digital dental models. Male's measured canine width and MCI is larger than female. SMCI anticipated erroneously 48% male and 51% female. Sex assessment via binary logistic regression (BLR) for inclusion of two (MD 43 and LICW) variables showed the overall percentage of prediction were 66.4%, and 64.1% and 68.8% for male and female respectively. BLR for inclusion of more tooth size (MD and BL widths of maxillary and mandibular right side) variables. The overall percentages of prediction were 75.4%, with 80% and 71% for male and female respectively. The Rao index is not a reliable way for the sex identification in Pakistani population. BLR sex prediction models is applicable for Pakistani population. [ABSTRACT FROM AUTHOR]

It is often difficult to predict the sex of an individual based on bloody incomplete footprints. However, such prints/impressions are particularly common in a crime scene. Again variability in the texture, color of the target surface has an impact on the bloodstained impression formed. The study of bare foot, footprint, footwear (i.e. shoe, canvas etc.) within the legal context is referred to as forensic podiatry. Based on the fact that it is possible to predict the sex of an individual from footprint impressions, an automated model has been proposed in this paper for analyzing the sex of an individual from his/her broken/incomplete footprint impressions based on morphological features alone. Five male and female volunteers aged between 20 to 65 years participated in dataset development. Keeping the blood volume constant and having stepped on differently shaped porcine blood pools, the individuals were asked to walk on herbarium sheets. The footprints were recorded and documented in accordance with the guidelines in place for physical evidence documentation within the forensic domain. The morphological features that were extracted from each of the footprint impressions are footprint length, footprint breadth, angle of walking, approximated heel radius etc. Using exhaustive cross validation technique, the dataset was divided into training and test set. Non-redundant, relevant features that are particularly effective at sex prediction were marked out using the relief algorithm in coherence with the correlation metric. Supervised learning techniques were used on the dataset to predict the sex of the owner of an unknown footprint. The study concentrates on morphological features in order to deal with bloodstain footprint transfer stains formed on any non-porous/non-absorbent surfaces such as cemented floor, glass, mosaic floor space, colored and designed tiled floor spaces. Features such as the angle of walking and foot breadth were found to be particularly influential in sex prediction from incomplete bloodstained foot sole impressions. In comparison to a system for sex prediction from complete footprint impressions (82.2%), the automated system developed on incomplete foot impressions recorded an accuracy level of 83.47%. [ABSTRACT FROM AUTHOR]

Background: While abundant research has been conducted on palatal rugae (PR), the literature pertaining to the sex dimorphism of the palatal rugae and their use for sex prediction is inconclusive. Moreover, palatal rugae have been classified into categories based on length, shape, direction and unification but accurate rugal morphometric linear and angular measurements have not yet been reported. Objective: The aims of this study were to -1- assess the dimensions and bilateral symmetry of the first three palatal rugae in an adult population and -2- explore sex dimorphism and the ability to predict sex from palatal rugae measurements. Materials and methods: The maxillary dental casts of 252 non-growing subjects (119 males, 130 females, mean age 25.6 ± 7.7 years) were scanned using a laser system (Perceptron ScanWorks® V5). Angular and linear transverse and anteroposteior measures of the first three palatal rugae were recorded. Independent samples t-tests and paired samples t-tests were used to test for side related differences and sex dimorphism. Multiple logistic regression was employed to model sex using associated palatal rugae measures. Results: Palatal rugae exhibited lateral asymmetry in the majority of bilateral measures. Males presented with larger values for 9 out of 28 parameters. Four linear rugae measurements and one angular measurement together correctly classified 71.4% of the subjects in their true gender. Conclusions: Morphometric palatal rugae measurements demonstrated promising usefulness in sex prediction. Recording morphometric linear and angular measures is recommended as an adjunct to the commonly used classification based on the shapes of rugae. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine sexual dimorphism as well as to predict sex using facial linear dimensions and angles among Hausas of Kano state Nigeria. A total of 283 subjects comprising 147 males and 136 females age range 18–25 years participated. Photographs methods were used to capture the face. Independent sample t -test was used to test for sex differences in the variables. Binary logistic regression was applied to obtain a predicting equation (BLR model) for sex. The predicted probabilities of BLR were analyzed using receiver operating characteristic curve. The results showed that all the facial linear dimensions showed significance sexual dimorphism except interocular distance, upper facial width, philtrum length, lower vermilion width, left and right orbital width. With regards to sex prediction, upper facial height was the single best predictor of sex with an accuracy of 76.2% and 24–33% contribution to the prediction. However, the percentage accuracy increased to 91% when six variables were pooled together in the equations. For facial angles, only nasion and aperture modified angle did not show significant gender differences. However, in the variables with significant sexual dimorphism only nasomental angle showed a significant level of sex prediction with an accuracy of 70.3%. In conclusion, sex discrimination using facial linear dimensions and angles was well established in this study. The sex of an individual of Hausa ethnic group can be determined using facial linear dimensions. Dispite sexual dimorphsm shown by facial angles, only nasomental angle was good discriminator of sex. [ABSTRACT FROM AUTHOR]

Objective: It has been suggested that the level of sexual dimorphism in deciduous dentition is relatively lower than that in permanent dentition. However, in sub adult skeletal remains whose osseous morphological traits of the sex have not defined yet, predicting sex on the basis of odontometric features may be the most precise technique. The aim of the present study was to assess the degree of sexual dimorphism in marginal enamel, dentin and pulp dimensions of second molar deciduous teeth in a pediatric population.Method and Materials: The present study was conducted on bitewing radiographs of 64 males and 60 females. The greatest width of enamel, dentin and pulp on mandibular and maxillary second molar deciduous teeth were measured. Student's t-test and discriminant analysis were used to compare the differences in the odontometric parameters between females and males.Results: Among the second molar measurements, only the maxillary pulp width did significantly discriminate the sex groups. The accuracy of sex identification of a case based on deciduous second molar tooth was approximately 68%.Conclusions: The application of second molar deciduous teeth in sex prediction showed moderate level of sexual dimorphism. In this respect, the maxillary pulp width had the greatest amount of contribution in sex discrimination. Therefore, these odontometric traits, in conjunction with other skeletal features, can be used as a supplementary sexing tool for gender prediction in forensic anthropology. [ABSTRACT FROM AUTHOR]

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices. [ABSTRACT FROM AUTHOR]

Most species of owls lack distinctive sexual color dimorphism, and plumage is not considered reliable for distinguishing sex. In North America, Long-eared Owls ( Asio otus) are generally considered monomorphic in color, although there are subtle color differences between the sexes. From 1987 to 2015, we investigated differences in plumage coloration of male and female Long-eared Owls in western Montana. We initially used an observational method (1987-1993), followed by a quantitative method (1994-1999), and then a simplified method (2000-2015). When we used the observational method, we correctly sexed all 22 Long-eared Owls. For the quantitative method, we used a Munsell Soil Color Chart to score underwing coverts, tarsometatarsus, and facial disc of breeding males and females and museum specimens purportedly sexed correctly. We found significant sex-specific color differences: underwing coverts (G = 136.77, df = 5, P < 0.01), tarsometatarsus ( G = 44.50, df = 4, P < 0.01), and facial disc ( G = 50.62, df = 7, P < 0.01). Underwing coverts differed the most between sexes. Based on these plumage color differences, we then correctly sexed all 19 owls captured during fall and winter and later recaptured as breeding birds. Using the simplified method, we correctly predicted the sex of 55 of 58 (93%) owls captured during fall and winter and later recaptured as breeders. Overall, we correctly predicted sex of 96 of 99 (96.9%) Long-eared Owls in Montana. We suggest that plumage coloration differences should be investigated in other study areas outside of Montana. [ABSTRACT FROM AUTHOR]

Sex estimation and the analysis of sexual dimorphism is an essential part of forensic and archaeological studies of skeletons. However, osteologists often have to rely on single measurements, such as femoral head diameters, to estimate sex, especially when skeletons are incomplete. We have obtained a sex-prediction model based on CT images by applying the logistic regression technique to the measurements obtained for the proximal femoral epiphyses and coxal. Nine variables for 114 Spaniards (58 females and 56 males) of known age and sex from a region close to Madrid have been studied. The prediction equation obtained using these nine variables correctly classifies 99.1 % of these individuals. Reducing the equation to the three most explanatory variables (VDH, HDH and MIB) resulted in the correct classification of 98.3 %. These findings suggest that this procedure is highly effective for sex prediction. However, a lack of expertise may produce biases in the measurements obtained from CT images. Moreover, these equations are only most effective for the population for which they were calculated as human growth and body size are sensitive to nutritional variations, environmental stress and the so-called secular trend. [ABSTRACT FROM AUTHOR]

Information about the sex of individuals is important for human identification. This study was conducted to quantify classification rates of sex prediction models for Malaysians using odontometric profiles. Mesiodistal (MD) and buccolingual (BL) crown dimensions of the permanent dentition were studied in 400 young adult Malaysians, giving a total of 28 tooth size variables. The sample consisted of three major ethnic groups, the Malays, Chinese and Tamils, since the aim was to assess sex dimorphism in Malaysians as a whole. Results showed that the mesiodistal diameter of the lower canine was the most sexually dimorphic dimension in Malaysian Malays and Tamils. Univariate analyses showed that the magnitude and pattern of sex dimorphism varies between these three ethnic groups, with Malaysian Chinese and Tamils being more dimorphic than the Malaysian Malays. Stepwise discriminant functions were generated bearing in mind their application in practical forensic situations. The range of classification rates was from 70.2% to 78.5% for the composite Malaysian group, and 83.8%, 77.9%, 72.4% for Malaysian Chinese, Malays and Tamils, respectively. The 'Area Under the Receiver Operating Characteristic Curve statistics' indicated good classification rates for three prediction models obtained using a combination of all tooth size variables, mandibular teeth, and mesiodistal dimensions in the composite Malaysian group, and for all tooth size variables in each ethnic group. The present study provides strong support for the value of odontometry as an adjunct scientific method for sex prediction in human identification. [ABSTRACT FROM AUTHOR]

Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson's correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson's correlation coefficient was 0.97 (p < 0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs. [ABSTRACT FROM AUTHOR]

I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE and 9–11th Centuries CE) in central Italy. A generalized low rank model (GLRM) was used to impute missing data and Area under the Curve—Receiver Operating Characteristic (AUC-ROC) with 20-fold stratified cross-validation was used to evaluate predictive performance of eight machine learning algorithms on different subsets of the data. Additional perspectives such as this one show strong potential for sex prediction in bioarchaeological and forensic anthropological contexts. Furthermore, GLRMs have the potential to handle missing data in ways previously unexplored in the discipline. Although results of this study look promising (highest AUC-ROC = 0.9722 for predicting binary male/female sex), the main limitation is that the sexes of the individuals included were not known but were estimated using standard macroscopic bioarchaeological methods. However, future research should apply this machine learning approach to known-sex reference samples in order to better understand its value, along with the more general contributions that machine learning can make to the reconstruction of past human lifeways. [ABSTRACT FROM AUTHOR]