Sensitivity assessment of the modified ABAcard® HemaTrace® and p30 immunochromatographic test cards.

Operational forensic laboratories routinely perform immunological assays for detecting various body fluids. The ABAcard® p30 and HemaTrace® immunochromatographic tests from Abacus Diagnostics are used for detecting the p30 enzyme in human semen and human haemoglobin present in blood respectively. In early 2023, manufacturer modifications to the ABAcard® p30 and HemaTrace® tests resulted in a reduction in card size and volume of sample extract used in the recommended protocol. This change in card design and/or the reduced volume of sample extract may alter the sensitivity of the test. This study established and compared the limit of detection (LOD) for the old and newly modified ABAcard® p30 and HemaTrace® test cards. The LOD values showed that the new test cards were approximately 2.4-fold (HemaTrace® test) and 3.4-fold (p30 test) more sensitive than the old cards. Additionally, it was found that the new HemaTrace® test cards were more susceptible to the high dose hook effect. In response to the increased sensitivity, existing data pertaining to the reactivity of these test cards to non-target body fluid and substances warrants re-investigation to ensure positive results are interpreted correctly.

Developing a Machine-Learning 'Smart' PCR Thermocycler, Part 1: Construction of a Theoretical Framework.

The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR 'looks like' at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR.

Developing a Machine Learning 'Smart' Polymerase Chain Reaction Thermocycler Part 2: Putting the Theoretical Framework into Practice.

The introduction of PCR into forensic science and the rapid increases in the sensitivity, specificity and discrimination power of DNA profiling that followed have been fundamental in shaping the field of forensic biology. Despite these developments, the challenges associated with the DNA profiling of trace, inhibited and degraded samples remain. Thus, any improvement to the performance of sub-optimal samples in DNA profiling would be of great value to the forensic community. The potential exists to optimise the PCR performance of samples by altering the cycling conditions used. If the effects of changing cycling conditions upon the quality of a DNA profile can be well understood, then the PCR process can be manipulated to achieve a specific goal. This work is a proof-of-concept study for the development of a smart PCR system, the theoretical foundations of which are outlined in part 1 of this publication. The first steps needed to demonstrate the performance of our smart PCR goal involved the manual alteration of cycling conditions and assessment of the DNA profiles produced. In this study, the timing and temperature of the denaturation and annealing stages of the PCR were manually altered to achieve the goal of reducing PCR runtime while maintaining an acceptable quality and quantity of DNA product. A real-time feedback system was also trialled using an STR PCR and qPCR reaction mix, and the DNA profiles generated were compared to profiles produced using the standard STR PCR kits. The aim of this work was to leverage machine learning to enable real-time adjustments during a PCR, allowing optimisation of cycling conditions towards predefined user goals. A set of parameters was found that yielded similar results to the standard endpoint PCR methodology but was completed 30 min faster. The development of an intelligent system would have significant implications for the various biological disciplines that are reliant on PCR technology.

Accounting for site-to-site DNA transfer on a packaged exhibit in an evaluation given activity level propositions.

Considering activity level propositions in the evaluation of forensic biology findings is becoming more common place. There are increasing numbers of publications demonstrating different transfer mechanisms that can occur under a variety of circumstances. Some of these publications have shown the possibility of DNA transfer from site to site on an exhibit, for instance as a result of packaging and transport. If such a possibility exists, and the case circumstances are such that the area on an exhibit where DNA is present or absent is an observation that is an important diagnostic characteristic given the propositions, then site to site transfer should be taken into account during the evaluation of observations. In this work we demonstrate the ways in which site to site transfer can be built into Bayesian networks when carrying out activity level evaluations of forensic biology findings. We explore the effects of considering qualitative vs quantitative categorisation of DNA results. We also show the importance of taking into account multiple individual's DNA being transferred (such as unknown or wearer DNA), even if the main focus of the evaluation is the activity of one individual.

Where did it go? A study of DNA transfer in a social setting.

The sensitivity of DNA analysis has progressed to the point that trace levels of DNA, originating from only a few cells, can generate informative profiles. This means that virtually any item or surface can be sampled with a reasonable chance of obtaining a DNA profile. As the presence of DNA does not suggest how it was deposited, questions are often raised as to how the DNA came to be at a particular location and the activity that led to its deposition. Therefore, understanding different modes of DNA deposition, reflective of realistic forensic casework situations, is critical for proper evaluation of DNA results in court. This study aimed to follow the movements of DNA to and from individuals and common household surfaces in a residential premises, while socially interacting. This took place over an hour and involved four participants, with known shedder status, designated as visitors (a male and a female) and hosts (a male and a female), who engaged in the activity of playing a board game while being served food. During the study, the participants were instructed to use the toilet on a single occasion to assess the transfer of DNA to new and unused underwear that was provided. All contacts made by the participants in the dining room and kitchen were video recorded to follow the movements of DNA. Samples were collected based on the history of contact, which included hands, fingernails and penile swabs. Direct contacts resulted in detectable transfer (LR > 1) in 87 % (87/100) of the non-intimate samples and clothing. For surfaces touched by multiple participants, DNA from the person who made the last contact was not always detectable. The duration and number of contacts did not significantly affect the detection of the person contacting the item. On the other hand, presence of background DNA and participant's shedder status appear to play an important role. Further, unknown contributors were detected in the majority of samples. Finally, indirect transfer was observed on a number of occasions including co-habiting partners of guests who were not present at the study location. The results of this study may assist with decision making for exhibit selection or targeting areas for sampling within the home environment. Our findings can also be used in conjunction with previous literature to develop activity-level evaluations in such situations where the source of the DNA is conceded, but the mode of deposition is disputed.

Extending the discussion on inconsistency in forensic decisions and results.

The subject of inter- and intra-laboratory inconsistency was recently raised in a commentary by Itiel Dror. We re-visit an inter-laboratory trial, with which some of the authors of this current discussion were associated, to diagnose the causes of any differences in the likelihood ratios (LRs) assigned using probabilistic genotyping software. Some of the variation was due to different decisions that would be made on a case-by-case basis, some due to laboratory policy and would hence differ between laboratories, and the final and smallest part was the run-to-run difference caused by the Monte Carlo aspect of the software used. However, the net variation in LRs was considerable. We believe that most laboratories will self-diagnose the cause of their difference from the majority answer and in some, but not all instances will take corrective action. An inter-laboratory exercise consisting of raw data files for relatively straightforward mixtures, such as two mixtures of three or four persons, would allow laboratories to calibrate their procedures and findings.

PCR in Forensic Science: A Critical Review.

The polymerase chain reaction (PCR) has played a fundamental role in our understanding of the world, and has applications across a broad range of disciplines. The introduction of PCR into forensic science marked the beginning of a new era of DNA profiling. This era has pushed PCR to its limits and allowed genetic data to be generated from trace DNA. Trace samples contain very small amounts of degraded DNA associated with inhibitory compounds and ions. Despite significant development in the PCR process since it was first introduced, the challenges of profiling inhibited and degraded samples remain. This review examines the evolution of the PCR from its inception in the 1980s, through to its current application in forensic science. The driving factors behind PCR evolution for DNA profiling are discussed along with a critical comparison of cycling conditions used in commercial PCR kits. Newer PCR methods that are currently used in forensic practice and beyond are examined, and possible future directions of PCR for DNA profiling are evaluated.

Emerging use of air eDNA and its application to forensic investigations - A review.

Biological material is routinely collected at crime scenes and from exhibits and is a key type of evidence during criminal investigations. Improvements in DNA technologies allow collection and profiling of trace samples, comprised of few cells, significantly expanding the types of exhibits targeted for DNA analysis to include touched surfaces. However, success rates from trace and touch DNA samples tend to be poorer compared to other biological materials such as blood. Simultaneously, there have been recent advances in the utility of environmental DNA collection (eDNA) in identification and tracking of different biological organisms and species from bacteria to naked mole rats in different environments, including, soil, ice, snow, air and aquatic. This paper examines the emerging methods and research into eDNA collection, with a special emphasis on the potential forensic applications of human DNA collection from air including challenges and further studies required to progress implementation.

Up in the air: Presence and collection of DNA from air and air conditioner units.

Biological material is routinely collected at crime scenes and from exhibits and is a key type of evidence during criminal investigations. Touch or trace DNA samples from surfaces and objects deemed to have been contacted are frequently collected. However, a person of interest may not leave any traces on contacted surfaces, for example, if wearing gloves. A novel means of sampling human DNA from air offers additional avenues for DNA collection. In the present study, we report on the results of a pilot study into the prevalence and persistence of human DNA in the air. The first aspect of the pilot study investigates air conditioner units that circulate air around a room, by sampling units located in four offices and four houses at different time frames post-cleaning. The second aspect investigates the ability to collect human DNA from the air in rooms, with and without people, for different periods of time and with different types of collection filters. Results of this pilot study show that human DNA can be collected on air conditioner unit surfaces and from the air, with air samples representing the more recent occupation while air conditioner units showing historic use of the room.

Predicting probative levels of touch DNA on tapelifts using Diamond™ Nucleic Acid Dye.

Tapelifting is a common strategy to recover touch DNA deposits from porous exhibits in forensic DNA casework. However, it is known that only about 30 % of tapelifts submitted for DNA analysis in operational forensic laboratories yield profiles suitable for comparison or upload to a searchable database. A reliable means to identify and remove non-probative tapelifts from the workflow would reduce sample backlogs and provide significant cost savings. We investigated whether the amount of macroscopic or microscopic fluorescence on a tapelift following staining with Diamond Nucleic Acid Dye (DD), determined using a Polilight and Dino Lite microscope respectively, could predict the DNA yield and/or the DNA profiling outcome using controlled (saliva), semi-controlled (finger mark) and uncontrolled (clothing) samples. Both macroscopic and microscopic DD fluorescence could predict DNA yield and profiling outcome for all sample types, however the predictive power deteriorated as the samples became less controlled. For tapelifts of clothing, which are operationally relevant, Polilight fluorescence scores were significantly impacted by clothing fibres and other non-cellular debris and could not be used to identify non-probative samples. The presence of less than 500 cells on a clothing tapelift using microscopic counting of stained corneocytes was identified as a potential threshold for a non-probative DNA profiling outcome. A broader examination of the reliability of this threshold using a casework trial is recommended. Due to the labour intensiveness of microscopic cell counting, and the increased risk of inadvertent contamination, automation of this process using image software in conjunction with artificial neural networks (ANN) should be explored.

A practical treatment of sensitivity analyses in activity level evaluations.

Evaluations of forensic observations considering activity level propositions are becoming more common place in forensic institutions. A measure that can be taken to interrogate the evaluation for robustness is called sensitivity analysis. A sensitivity analysis explores the sensitivity of the evaluation to the data used when assigning probabilities, or to the level of uncertainty surrounding a probability assignment, or to the choice of various assumptions within the model. There have been a number of publications that describe sensitivity analysis in technical terms, and demonstrate their use, but limited literature on how that theory can be applied in practice. In this work we provide some simplified examples of how sensitivity analyses can be carried out, when they are likely to show that the evaluation is sensitive to underlying data, knowledge or assumptions, how to interpret the results of sensitivity analysis, and how the outcome can be reported. We also provide access to an application to conduct sensitivity analysis.

A diagnosis of the primary difference between EuroForMix and STRmix™.

There is interest in comparing the output, principally the likelihood ratio, from the two probabilistic genotyping software EuroForMix (EFM) and STRmix™. Many of these comparison studies are descriptive and make little or no effort to diagnose the cause of difference. There are fundamental differences between EFM and STRmix™ that are causative of the largest set of likelihood ratio differences. This set of differences is for false donors where there are many instances of LRs just above or below 1 for EFM that give much lower LRs in STRmix™. This is caused by the separate estimation of parameters such as allele height variance and mixture proportion using MLE under Hp and Ha for EFM. This can result in very different estimations of these parameters under Hp and Ha . It results in a departure from calibration for EFM in the region of LRs just above and below 1.

Estimation of population specific values of theta for sequence-based STR profiles.

We describe the estimation of θ (theta) values from autosomal STR sequencing data for five metapopulations. The data were compiled from 20 publications and included 39 datasets comprising a total of 7005 samples. The estimates are suitable for use within the calculation of match probabilities in forensic casework. We also have constructed a phylogenetic tree using this data that aligns with our understanding of human evolution.

Determining the number and size of background samples derived from an area adjacent to the target sample that provide the greatest support for a POI in a target sample.

When sampling an item or surface for DNA originating from an action of interest, one is likely to collect DNA unrelated to the action of interest (background DNA). While adding to the complexity of a generated DNA profile, background DNA has been shown to aid in resolving the genotypes of contributors in a targeted sample, and where references of donors to the background DNA are not available, strengthen the LR supporting a person of interest contributing to the targeted sample. This is possible thanks to advances in probabilistic genotyping, where forensic labs are able to deconvolute complex DNA profiles to obtain lists of genotypes and their associated weights. Coupled with DBLR™, one can then compare multiple evidentiary profiles to each other to determine the contribution of common, but unknown, contributors. Here, we consider factors associated with taking background samples and whether one should collect multiple background samples that all relate to a single target sample, or if one should collect larger background samples rather than smaller samples. Background samples consisted of DNA accumulated on the items primarily by one or both occupants of a single household, while targeted samples were generated from touch deposits, or saliva deposits that had been left to air dry. Samples were collected from areas of various sizes, consisting of only the background, the target and the background directly beneath it, and the target and additional surrounding background. A broad range of DNA quantities were recovered, with larger background samples (400 cm2) yielding significantly more DNA than smaller background samples (30 cm2). Significant differences in DNA quantities between target samples were not observed. Generated DNA profiles were interpreted using STRmix™ and DBLR™, and where there was support for a common donor between the background and target sample, pairwise comparisons were performed to observe the effect on the LR supporting the target DNA donor contributing to the targeted sample when conditioning on one (or two) common donor between the targeted sample and 1-8 background samples. Multiple background samples gave significantly higher LRs compared to a single background sample, the larger sampled background area resulted in larger LR gains than the smaller areas, and four or more background samples reduced LR variability considerably. Here we provide recommendations for the minimum and ideal number of additional background samples that should be collected, and that several smaller samples may be more beneficial than a single larger sample.

The secret hidden in dust: Assessing the potential to use biological and chemical properties of the airborne fraction of soil for provenance assignment and forensic casework.

The airborne fraction of soil (dust) is both ubiquitous in nature and contains localised biological and chemical signatures, making it a potential medium for forensic intelligence. Metabarcoding of dust can yield biological communities unique to the site of interest, similarly, geochemical analyses can uncover elements and minerals within dust that can be matched to a geographic location. Combining these analyses presents multiple lines of evidence as to the origin of dust collected from items of interest. In this work, we investigated whether bacterial and fungal communities in dust change through time and whether they are comparable to soil samples of the same site. We integrated dust metabarcoding into a framework amenable to forensic casework, (i.e., using calibrated log-likelihood ratios) to predict the origin of dust samples using models constructed from both dust samples and soil samples from the same site. Furthermore, we tested whether both metabarcoding and geochemical/mineralogical analyses could be conducted on a single swabbed sample, for situations where sampling is limited. We found both analyses could generate results from a single swabbed sample and found biological and chemical signatures unique to sites. However, we did find significant variation within sites, where this did not always correlate with time but was a random effect of sampling. This variation within sites was not greater than between sites and so did not influence site discrimination. When modelling bacterial and fungal diversity using calibrated log-likelihood ratios, we found samples were correctly predicted using dust 67% and 56% of the time and using soil 56% and 22% of the time for bacteria and fungi communities respectively. Incorrect predictions were related to within site variability, highlighting limitations to assigning dust provenance using metabarcoding of soil.

Addressing uncertain assumptions in DNA evidence evaluation.

Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant's profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.

Carrying out common DNA donor analysis using DBLR™ on two or five-cell mini-mixture subsamples for improved discrimination power in complex DNA mixtures.

Probabilistic genotyping systems are able to analyse complex mixed DNA profiles and show good power to discriminate contributors from non-contributors. However, the abilities of the statistical analyses are still unavoidably bound by the quality of information being analysed. If a profile has a high number of contributors, or a contributor that is present in trace amounts, then the amount of information about those individuals in the DNA profile is limited. Recent work has shown the ability to gain better resolution of the genotypes of contributors to complex profiles using cell subsampling. This is the process of taking many sets of a limited number of cells and individually profiling each set. These 'mini-mixtures' can provide greater information about the genotypes of underlying contributors. In our work we take the resulting profiles from multiple subsamplings of complex DNA profiles in equal amounts and show how testing for, and then assuming, a common DNA donor can further improve the ability to resolve the genotypes of contributors. Using direct cell sub-sampling and statistical analysis software DBLR™, we were able to recover single source profiles of uploadable quality from five out of the six contributors of an equally proportioned mixture. Through the analysis of mixtures in this work we provide a template for carrying out common donor analysis for maximum effect.

A lights-out forensic DNA analysis workflow for no-suspect crime.

An automated system of DNA profile processing (termed a 'lights-out' workflow) was trialled for no-suspect cases over a three-month period at Forensic Science SA (FSSA). The lights-out workflow utilised automated DNA profile reading using the neural network reading feature in FaSTR™ DNA with no analytical threshold. The profile information from FaSTR™ DNA was then processed in STRmix™ using a top-down analysis and automatically compared to a de-identified South Australian searchable DNA database. Computer scripts were used to generate link reports and upload reports and these were compared to the links and uploads that were obtained for the cases during their standard processing within the laboratory. The results of the lights-out workflow was an increase in both uploads and links compared to the standard workflow, with minimal adventitious links or erroneous uploads. Overall, the proof-of-concept study shows the potential for using automated DNA profile reading and top-down analysis to improve workflow efficiency in a no-suspect workflow.

Extending the discrete Laplace method: incorporating multi-copy loci, partial repeats and null alleles.

The discrete Laplace method can be used to estimate the frequency of a Y-chromosomal STR haplotype using a random sample from the population. Two limitations of the method are the assumptions that each profile has exactly one allele at every locus and that this allele has an integer repeat number. We relax these assumptions to allow for multi-copy loci, partial repeats and null alleles. We show how the parameters to the extension of the model can be estimated by numerical optimisation using an off-the-shelf solver. Concordance with the discrete Laplace method is obtained when the data satisfy the more stringent assumptions of the original method. We also investigate the performance of the (extended) discrete Laplace method when used to assign match probabilities for haplotypes. A simulation study shows that as more loci are used, match probabilities are underestimated more severely. This is consistent with the hypothesis that the discrete Laplace method cannot model the matches that arise by being identical by descent (IBD). As the number of loci increases the fraction of matches that are IBD increases. Simulation provides support that the discrete Laplace can model those matches that arise from identity by state (IBS) only.