Nurtami Soedarsono

Allele frequency data for 15 autosomal STR loci in eight Indonesian subpopulations

Evolutionary and cultural history can affect the genetic characteristics of a population and influences the frequency of different variants at a particular genetic marker (allele frequency). These characteristics directly influence the strength of forensic DNA evidence and make the availability of suitable allele frequency information for every discrete country or jurisdiction highly relevant. Population sub-structure within Indonesia has not been well characterised but should be expected given the complex geographical, linguistic and cultural architecture of the Indonesian population. Here we use forensic short tandem repeat (STR) markers to identify a number of distinct genetic subpopulations within Indonesia and calculate appropriate population sub-structure correction factors. This data represents the most comprehensive investigation of population sub-structure within Indonesia to date using these markers. The results demonstrate that significant sub-structure is present within the Indonesian population and must be accounted for using island specific allele frequencies and corresponding sub-structure correction factors in the calculation of forensic DNA match statistics.

Comparison of tumor necrosis Factor-α concentrations in gingival crevicular fluid between self-ligating and preadjusted edgewise appliances in the early leveling stage of orthodontic treatment

Introduction: Tumor necrosis factor-α (TNF-α) is an important proinflammatory cytokine that regulates the early phase of inflammation reaction during orthodontic tooth movement. The aim of the present study was to compare TNF-α concentrations in the gingival crevicular fluid (GCF) between preadjusted edgewise appliance (PEA) and self-ligating (SL) systems during the early leveling stage of orthodontic treatment. Materials and Methods: Eighteen patients (aged 15–35 years) who participated in this study were divided into two experimental groups (PEA and SL) and control group (without orthodontic treatment). The GCF was taken at five sites in the maxilla anterior teeth from each participant just before bracket bonding and at 1, 24, and 168 h after the initiation of tooth movement. Cytokine levels were determined through ELISA. Results: The concentration of TNF-α was significantly higher in the experimental groups than in the control group at 24 h after force application. TNF-α levels were significantly decreased at 168 h after force application in the PEA group. Meanwhile, in the SL group, the level of TNF-α at 168 h was still increased, although there was no statistically significant difference. Conclusion: TNF-α concentration was increased at 1 h and 24 h after orthodontic force application in both the PEA and SL groups. In the PEA group, TNF-α concentration was significantly decreased at 168 h, meanwhile in the SL group, this value remained increased at this time point. The differences in TNF-α concentration between the PEA and SL groups may be caused by their different types of brackets, wires, and ligation methods.

Gaussian Fuzzy Number for STR-DNA Similarity Calculation Involving Familial and Tribal Relationships

We performed locus similarity calculation by measuring fuzzy intersection between individual locus and reference locus and then performed CODIS STR-DNA similarity calculation. The fuzzy intersection calculation enables a more robust CODIS STR-DNA similarity calculation due to imprecision caused by noise produced by PCR machine. We also proposed shifted convoluted Gaussian fuzzy number (SCGFN) and Gaussian fuzzy number (GFN) to represent each locus value as improvement of triangular fuzzy number (TFN) as used in previous research. Compared to triangular fuzzy number (TFN), GFN is more realistic to represent uncertainty of locus information because the distribution is assumed to be Gaussian. Then, the original Gaussian fuzzy number (GFN) is convoluted with distribution of certain ethnic locus information to produce the new SCGFN which more represents ethnic information compared to original GFN. Experiments were done for the following cases: people with family relationships, people of the same tribe, and certain tribal populations. The statistical test with analysis of variance (ANOVA) shows the difference in similarity between SCGFN, GFN, and TFN with a significant level of 95%. The Tukey method in ANOVA shows that SCGFN yields a higher similarity which means being better than the GFN and TFN methods. The proposed method enables CODIS STR-DNA similarity calculation which is more robust to noise and performed better CODIS similarity calculation involving familial and tribal relationships.

Fuzzy logic System for DNA Profile Matching with Embedded Ethnic Inference

This paper proposes a novel technique to do probabilistic inference by using simple Fuzzy logic System (FlS), especially ethnic information in DNA profile matching algorithm. By using the allele marker’s distribution probability density function as the membership function in the FlS, the new technique makes it possible to tell the ethnic similarity between two DNA profiles in a fast and simple way. A data acquired from ethnic groups of Indonesia is used to test the technique and produced promising result, being able to indicate higher ethnic similarity score within an ethnic group and lower similarity between ethnic groups. However, further research is needed to further improve the model and warrant the correctness and accuracy, especially because the data is not very discriminative.