Although molecular techniques evolved considerably in last years, anthropological methods of assessing skeletal remains, continues to be an important tool in the identification process in medico legal investigations. The objective of this study was to develop a discriminant function equation for estimating sex and stature using several measurements of lumbar vertebrae in a Thai population. We studied 150 lumbar columns (75 male and 75 female) age range of 22 to 89 years from the Forensic Osteology Research Center, Chiang Mai University, Thailand. The quantitative variables with sex were analyzed by the discriminant function analysis and that with stature were calculated using linear regression. The pixel density of the major axis of the trabecular surface of superior endplate of the first lumbar vertebra had the most accuracy in sex determination. The regression equation with quantitative variables in stature estimation described 32.3 % of the total variance with standard error of estimate of 7.736 cm. Lumbar vertebrae can be used as part of the stature and sex quantitatively and qualitatively estimating in Thais incomplete skeletal remains.

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Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep learning and morphometric technique to perform sex determination from lumbar vertebrae in a Thai population. A total of 1100 lumbar vertebrae (L1-L5) from 220 Thai individuals (110 males and 110 females) were obtained from the Forensic Osteology Research Center, Faculty of Medicine, Chiang Mai University, Thailand. In addition, two linear measurements of superior and inferior endplates from the digital caliper and image analysis were carried out for morphometric technique. Deep learning applied image classification to the superior and inferior endplates of the lumbar vertebral body. All lumbar vertebrae images are included in the dataset to increase the number of images per class. The accuracy determined the performance of each technique. The results showed the accuracies of 82.7%, 90.0%, and 92.5% for digital caliper, image analysis, and deep learning techniques, respectively. The lumbar vertebrae L1-L5 exhibit sexual dimorphism and can be used in sex estimation. Deep learning is more accurate in determining sex than the morphometric method. In addition, the subjectivity and errors in the measurement are decreased. Finally, this study presented an alternative approach to determining sex from lumbar vertebrae when the more traditionally used skeletal elements are incomplete or absent.

Until recently the genus Didymocarpus Wall. (Gesneriaceae) was used in an unwarrantably wide sense and included more than 180 species. It has now been remodelled and restricted to around 70 species. Of these, 18 species and one variety are known to occur in Thailand. To clarify the relationships among Thai species of Didymocarpus we sequenced the internal transcribed spacers (ITS) of nuclear ribosomal DNA (nrDNA) from a sample of 23 taxa, including 15 from Thailand, four from China, three from Malaysia and one from Bhutan. Seventeen morphological characters were coded for all 23 taxa and optimized onto a retention index (RI) reweighted maximum parsimony (MP) tree. The phylogenetic analyses suggested that Didymocarpus taxa formed a strongly supported monophyletic clade, with several supported subclades. The combination of molecular phylogeny and optimization of morphological characters suggests the presence of three distinct groups: the first, corresponding to Didymocarpus sect. Elati Ridl., includes plants with tall stems, yellow or white flowers and one-celled conoid or two-celled headed pigment glands; the other two groups, which represent Didymocarpus sect. Didymocarpus, both contain plants with dwarfed stems and violet or purple flowers, but are distinguished by the presence of both four-celled conoid or onecelled globose glands in one, and the absence in the other. Optimization of geographical locality onto the phylogeny led us to propose the hypothesis that, based on this sample, the geographical origin of Didymocarpus is the Malay Peninsula, and the ancestral corolla colour is white/yellow. Subsequent dispersal northward through southern and northern Thailand to China and Bhutan was accompanied by the evolution of a purple/violet corolla colour.

A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k-fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15-and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets.