Barash, M, McNevin, D, Fedorenko, V & Giverts, P 2024, 'Machine learning applications in forensic DNA profiling: A critical review', Forensic Science International: Genetics, vol. 69, pp. 102994-102994.
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Brown, AO, Green, PJ, Frankham, GJ, Stuart, BH & Ueland, M 2024, 'Correction to “Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical “-omics” Data with Multiple Explanatory Variables”', ACS Omega, vol. 9, no. 13, pp. 15724-15724.
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Dawson, BM, Ueland, M, Carter, DO, Mclntyre, D & Barton, PS 2024, 'Bridging the gap between decomposition theory and forensic research on postmortem interval', International Journal of Legal Medicine, vol. 138, no. 2, pp. 509-518.
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AbstractKnowledge of the decomposition of vertebrate animals has advanced considerably in recent years and revealed complex interactions among biological and environmental factors that affect rates of decay. Yet this complexity remains to be fully incorporated into research or models of the postmortem interval (PMI). We suggest there is both opportunity and a need to use recent advances in decomposition theory to guide forensic research and its applications to understanding the PMI. Here we synthesise knowledge of the biological and environmental factors driving variation in decomposition and the acknowledged limitations among current models of the PMI. To guide improvement in this area, we introduce a conceptual framework that highlights the multiple interdependencies affecting decay rates throughout the decomposition process. Our framework reinforces the need for a multidisciplinary approach to PMI research, and calls for an adaptive research cycle that aims to reduce uncertainty in PMI estimates via experimentation, modelling, and validation.
Fursman, H, Finch, E, Xiao, L, Lefrançois, E, Gupta, A, Bartlett, M, Latimer, J, Chadwick, S, Roux, C & Morelato, M 2024, 'A snapshot of injecting drug consumption from the analysis of used syringes within the Medically Supervised Injecting Centre in Sydney, Australia', Drug and Alcohol Review, vol. 43, no. 3, pp. 787-798.
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AbstractIntroductionThe administration of illicit drugs by injection is associated with considerable harm, including an increased risk of overdose. The chemical analysis of used syringes can enhance knowledge on injecting drug consumption beyond traditional data sources (self‐report surveys). This additional information may be useful during significant global events like the COVID‐19 pandemic. This study aimed to examine a snapshot of the drugs injected at the Medically Supervised Injecting Centre (MSIC) in Sydney, Australia, in 2019–2020.MethodsUsed syringes were collected from MSIC across three periods throughout 2019 and 2020 (February 2019, March—April 2020 and June—September 2020). Drug residues were extracted from used syringes using methanol before detection by gas chromatography—mass spectrometry and ultra‐performance liquid chromatography—tandem mass spectrometry. The chemical analysis results were compared to self‐report data obtained from MSIC clients.ResultsHeroin (46–53%), methamphetamine (24–34%) and pharmaceutical opioids (15–27%) were the most common drug residues detected. The chemically detected drugs had declining coherence with the drugs self‐reported by MSIC clients across the time periods examined.Discussion and ConclusionsThere was no significant change in the drugs injected (heroin, methamphetamine and pharmaceutical opioids) across the three periods collected throughout varying COVID‐19 lockdown restrictions. Changes in the frequency of other drugs injected and discrepancies between chemical analysis and self‐report were potentially related to regulatory changes, degradation or misinformed sales. Routine chemical analysis of used syringes has provided an alternative informatio...
Karami, H, Kamruzzaman, M, Covington, JA, Hassouna, MÉ, Darvishi, Y, Ueland, M, Fuentes, S & Gancarz, M 2024, 'Advanced evaluation techniques: Gas sensor networks, machine learning, and chemometrics for fraud detection in plant and animal products', Sensors and Actuators A: Physical, vol. 370, pp. 115192-115192.
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Liu, T, Zhang, W, Wang, L, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2024, 'Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-15.
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Neuteboom, W, Ross, A, Bugeja, L, Willis, S, Roux, C & Lothridge, K 2024, 'Quality Management in forensic science: A closer inspection', Forensic Science International, vol. 358, pp. 111779-111779.
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Qashlan, A, Nanda, P & Mohanty, M 2024, 'Differential privacy model for blockchain based smart home architecture', Future Generation Computer Systems, vol. 150, pp. 49-63.
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Stuart, B, Frankham, GJ, Mangion, G & Ueland, M 2024, 'A rapid and non-destructive identification method for tortoiseshell products using attenuated total reflectance infrared spectroscopy', Forensic Science International: Animals and Environments, vol. 5, pp. 100079-100079.
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Stuart, B, Guan, J, Collins, S, Thomas, P & Ueland, M 2024, 'A preliminary study of non-woven fabrics for forensic identification purposes', Australian Journal of Forensic Sciences, vol. 56, no. 2, pp. 144-153.
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While traditional woven textiles have been the subject of many forensic investigations, non-woven fabrics have received minimal attention thus far. Given the expansion of commercial applications of non-woven fabrics, a preliminary investigation of household wipes has been carried out to characterize the compositions of these widely available non-woven fabrics. Infrared spectroscopy and thermogravimetric analysis were employed to identify the fibre type and additives of three types of commercial wipes. Polyester and/or viscose fibres were found to be the main components and, along with the identification of binders, enable source types to be differentiated. The predicted different sensitivities of the fibre types to biodeterioration highlights the importance of future environmental studies for the correct characterization of non-woven fabrics in evidence.
Thurn, B, Schotsmans, EMJ & Ueland, M 2024, 'Lime and odour: A preliminary investigation into the effect of hydrated lime on the volatiles emitted from human remains', Forensic Science International, vol. 358, pp. 111745-111745.
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Ward, J, Coakley, L, Grisedale, K, Seddon, S, Spiden, M, Watson, JL & McNevin, D 2024, 'Operationalization of the National DNA Program for Unidentified and Missing Persons' Forensic Investigative Genetic Genealogy Capability for Human Remains Identification in Australia', Forensic Genomics, vol. 4, no. 1, pp. 32-40.
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Waszczuk, JR, Peacock, L, Chadwick, S, Maynard, P, Raymond, J & Roux, C 2024, 'Misidentifications of alphanumeric characters in serial number restorations', Australian Journal of Forensic Sciences, vol. 56, no. 2, pp. 168-188.
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Chemical etching has long been established as a reliable means of recovering defaced characters such as serial numbers. Although successful restorations can present clearly defined characters, a preliminary assessment of proficiency test results shows varied participant interpretations of samples marked with the same characters. Thus, a study was completed to determine if specific alphanumeric characters are more likely to be misidentified, comprising a practical exercise and analysis of 16 compiled proficiency test results. The practical exercise involved 64 steel plates stamped with six randomized characters that were removed and chemically restored. Participants individually assessed the plates and provided their interpretations. A total of 39 participants of various backgrounds and experience in serial number restorations were used in this study. After comparing participant interpretations with the ground truth, characters more prone to be misidentified were determined. The proficiency test results showed that the most frequently misidentified characters were the letters B and N, among a limited set. Whilst the practical assessment found a notable increase in misidentifications for the letters G, Q and S. Interestingly, none of these characters were used in the proficiency tests. It is hoped that the results of this study raise awareness for forensic examiners undertaking restorations.
Watson, J, McNevin, D, Grisedale, K, Spiden, M, Seddon, S & Ward, J 2024, 'Operationalisation of the ForenSeq® Kintelligence Kit for Australian unidentified and missing persons casework', Forensic Science International: Genetics, vol. 68, pp. 102972-102972.
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Single nucleotide polymorphism (SNP) genotyping technologies can generate investigative leads for human remains identification, including estimation of biological sex, biogeographical ancestry (BGA), externally visible characteristics (EVCs), identity, uniparental lineage and extended kinship. The ForenSeq® Kintelligence Kit provides forensic laboratories with the ability to apply this suite of genetic tools to forensic samples using one panel targeting 10,230 SNPs (including 56 ancestry-informative, 24 phenotype-informative, 94 identity-informative, 106 X chromosome, 85 Y chromosome and 9867 kinship-informative SNPs) sequenced on the MiSeq FGx® Sequencing System. The ForenSeq® Kintelligence Kit has been internally validated, optimised and operationalised by the Australian Federal Police National DNA Program for Unidentified and Missing Persons (AFP Program) for coronial casework. The internal validation was conducted according to the Scientific Working Group on DNA Analysis Methods guidelines (excluding mixture analysis), focussing on sample types typically encountered in human remains identification casework, such as bones, teeth, nail, blood and hair. The workflow was optimised for a high throughput library preparation and sequencing workflow, and additional analytical thresholds were developed to improve genotyping accuracy for low DNA input samples. Additionally, the genetic intelligence generated from the kit was compared to the self-declared biological sex, EVCs and BGA of the DNA donors to assess concordance. The kit was able to produce high quality SNP profiles from 1.0 ng down to 0.1 ng of DNA, with high repeatability and reproducibility, and minimal background noise. The prediction accuracy for biological sex (95%), hair colour (58%), eye colour (74%) and BGA inferences (consistent: 74%; partially consistent: 10%; inconclusive: 16%) was determined based on self-declared data. Additionally, SNP profiles from a volunteer family group of ten rel...
Watson, JL, McNevin, D & Ward, J 2024, 'Genetic Kinship Testing Techniques for Human Remains Identification and Missing Persons Investigations', Forensic Genomics, vol. 4, no. 1, pp. 4-23.
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Weyermann, C, Willis, S, Margot, P & Roux, C 2024, 'Response to the letter to the Editor commenting on “Towards more relevance in forensic science research and development” by Weyermann et al.', Forensic Science International, vol. 355, pp. 111928-111928.
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