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There is a need to understand the occurrence of drop-in and their peak height distributions to further improve existing models to interpret complex DNA results. The necessary data is usually very time-consuming to collect. Here we present a practical approach for simultaneous characterisation of reproducible artefacts and drop-in contamination from negative controls. As an alternative to the traditional approach we demonstrate the use of the DEPArray system as a source of single molecules for amplification to infer the peak height distribution of drop-in events. Though signal detection is often confounded by noise and artefact, single molecule contamination, or allele drop-in, is also regularly detected as demonstrated by the overlap in signal intensities between suspected drop-in events and signal obtained from single-cells. [ABSTRACT FROM AUTHOR]

In this paper are reported the results of an experimental comparative analysis between R134a and HFO1234ze, when they are employed as refrigerants in a domestic refrigerator. Under sub-tropical conditions, it have been measured the energy consumptions in accordance with the UNI-EN-ISO15502. For each experiment it have been estimated both the TEWI and the Life Cycle Climate Performance indexes to assess the environmental impact due to HFO1234ze when used in place of R134a. [ABSTRACT FROM AUTHOR]

The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors. The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a ‘gold standard’ LR, to the LR that would be obtained in casework. The ‘gold standard’ LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The ‘casework LR’ was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim . We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework. [ABSTRACT FROM AUTHOR]

Abstract: There has been very little work published on the variation of reporting practices of mixtures between laboratories, but it has been previously demonstrated that there is little consistency. This is because there is no current uniformity of practice, so different laboratories will operate using different rules. The interpretation of mixtures is not solely a matter of using some software to provide ‘an answer’. An assessment of a case will usually begin with a consideration of the circumstances of a crime. Assumptions made about the numbers of contributors follow from an examination of the electropherogram(s) – and these may differ between the prosecution and the defence hypotheses. There may be a necessity to evaluate several sets of hypotheses for any given case if the circumstances are uncertain. Once the hypotheses are formulated, the mathematical analysis is complex and can only be accomplished by the use of specialist software. In order to obtain meaningful results, it is essential that scientists are trained, not only in the use of the software, but also in the methodology to understand the likelihood ratio concept that is used. The Euroforgen-NoE initiative has developed a training course that utilizes the LRmix program to carry out the calculations. This software encompasses the recommendations of the ISFG DNA commissions on mixture interpretation and is able to interpret samples that may come from two or more contributors and may also be partial profiles. Recently, eighteen different laboratories were trained in the methodology. Afterwards they were asked to independently analyze two different cases with partial mixture DNA evidence and to write a statement court-report. We show that by introducing a structured training programme, it is possible to demonstrate, for the first time, that a high degree of standardization, leading to uniformity of results can be achieved by participating laboratories. [Copyright &y& Elsevier]

Abstract: The interpretation of DNA mixtures has proven to be a complex problem in forensic genetics. In particular, low template DNA samples, where alleles can be missing (allele drop-out), or where alleles unrelated to the crime-sample are amplified (allele drop-in), cannot be analysed with classical approaches such as random man not excluded or random match probability. Drop-out, drop-in, stutters and other PCR-related stochastic effects, create uncertainty about the composition of the crime-sample, making it difficult to attach a weight of evidence when (a) reference sample(s) is (are) compared to the crime-sample. In this paper, we use a probabilistic model to calculate likelihood ratios when there is uncertainty about the composition of the crime-sample. This model is essentially exploratory in the sense that it allows the exploration of LRs when two key-parameters, drop-out and drop-in are varied within their plausible ranges of variation. We build on the work of Curran et al. [8], and improve their probabilistic model to allow more flexibility in the way the model parameters are applied. Two new main modifications are brought to their model: (i) different drop-out probabilities can be applied to different contributors, and (ii) different parameters can be used under the prosecution and the defence hypotheses. We illustrate how the LRs can be explored when the drop-out and drop-in parameters are varied, and suggest the use of Monte Carlo simulations to derive plausible ranges for the probability of drop-out. Although the model is suited for both high and low template samples, we illustrate the advantages of the exploratory approach through two DNA mixtures (involving two and at least three individuals) with low template components. [Copyright &y& Elsevier]

Abstract: DNA typing protocols have been improved over the last few years and even mixtures from minute and low grade DNA stains do not necessarily preclude typing. Nevertheless, in those cases stochastic phenomena tend to hamper interpretation. In order to supplement the current discussion about the interpretation of such challenging data, we focused on different combinations of analyses as an attempt to overcome stochastic problems. We analyzed mixtures of two types of degraded DNA in low template amounts (50 and 100pg DNA per contributor) using four types of multiplex STR typing kits: PowerPlex® ESX 17, PowerPlex® ESI 17, Investigator® ESSplex SE Kit and P11, a non-commercial kit. We employed the results of double or triple analyses for a comparison of different types of interpretation rules based on reporting either only reproducible alleles (consensus interpretation) or all alleles, even if they are only observed once (composite interpretation). The interpreted and composed profiles were compared to the known alleles of the contributors and a “degree of validity” was calculated. When only two single amplifications were taken into account, we observed a higher degree of validity for composite profiles. The difference for consensus interpretation could be compensated when a minimum of three amplifications were carried out. Using the same kit for repeat analyses increases the chances to yield reproducible results required for consensus interpretation. Combining different kits in a complementing approach, on the other hand, offers the opportunity to reduce the number of drop-out alleles: differences in amplicon lengths for specific markers between kits can increase the resulting information. In the case of a few amplifications available this effect might only be visible with the composite method. Several markers like SE33 are particularly affected by this. Based on our observations the consensus interpretation method may not reflect the original profile in an optimal way in some special cases like low template, degraded mixture stains. In those cases the composite interpretation could yield more complete results. However, such a composite profile should be used with caution and only for limited purposes. Generally recommending the consensus interpretation thus seems not to be justified: a more differentiated approach appears to be worthwhile, e.g. the amount of drop-outs, the number of replicates, choice and combination of kits and even a marker specific procedure might be taken into account. [Copyright &y& Elsevier]

Abstract: In this paper we critically examine the causes of the underlying confusion that relates to the issue of low-template (LT) DNA profile interpretation. Firstly, there is much difficulty in attempting to distinguish between LT-DNA vs. conventional DNA because there is no discrete ‘cut-off’ point that can be reasonably defined or evaluated. LT-DNA is loosely characterised by drop-out (where alleles may be missing) and drop-in (where additional alleles may be present). We have previously described probabilistic methods that can be used to incorporate these phenomena using likelihood ratio (LR) principles. This is preferred to the random man not excluded (RMNE) method, because we cannot identify a coherent way forward within the restrictions provided by this framework. Most LT-DNA profiles are interpreted using a ‘consensus’ profile method, we called this the ‘biological model’, where only those alleles that are duplicated in consecutive tests are reported. We recognise that there is an increased need for probabilistic models to take precedence over the biological model. These models are required for all kinds of DNA profiles, not just those that are believed to be low-template. We also recognise that there is a need for education and training if the methods we recommend are to be widely introduced. [Copyright &y& Elsevier]

Abstract: We discuss the interpretation of DNA profiles obtained from low template DNA samples. The most important challenge to interpretation in this setting arises when either or both of “drop-out” and “drop-in” create discordances between the crime scene DNA profile and the DNA profile expected under the prosecution allegation. Stutter and unbalanced peak heights are also problematic, in addition to the effects of masking from the profile of a known contributor. We outline a framework for assessing such evidence, based on likelihood ratios that involve drop-out and drop-in probabilities, and apply it to two casework examples. Our framework extends previous work, including new approaches to modelling homozygote drop-out and uncertainty in allele calls for stutter, masking and near-threshold peaks. We show that some current approaches to interpretation, such as ignoring a discrepant locus or reporting a “Random Man Not Excluded” (RMNE) probability, can be systematically unfair to defendants, sometimes extremely so. We also show that the LR can depend strongly on the assumed value for the drop-out probability, and there is typically no approximation that is useful for all values. We illustrate that ignoring the possibility of drop-in is usually unfair to defendants, and argue that under circumstances in which the prosecution relies on drop-out, it may be unsatisfactory to ignore any possibility of drop-in. [Copyright &y& Elsevier]

• Data from 28,842 negative control samples were reviewed for the presence of allele drop-in. • Allele drop-in, may at times be associated with a genomic contamination event associated with a single individual. • Experimental data were generated that produce drop-in like events from a single genomic source. • Contamination manifesting as drop-in, identifiable to a single individual was identified in live case work negative controls. Some probabilistic mixture programmes take into account the presence of additional alleles by utilising drop-in models [1–4]. Although the precise details of the various models vary, at their core, they all rely on two basic assumptions – (1) that drop-in events occur independently of each other and (2) the frequency of individual dropped-in alleles mirrors the composition within some specified population. In order to examine the robustness of these assumptions, we have collected data on allele drop-in and contamination events in 28,842 negative control samples processed over a three year period in our DNA crime laboratory. These data were used to characterise drop-in events, and to identify trends in drop-in rates over time and between control types. In addition, we carried out an experiment using genomic DNA that had been highly diluted and demonstrate that, at these levels, drop-in events become indistinguishable from low level genomic contamination. Our results show that drop-in alleles are not necessarily independent random events. Moreover, a comparison between our data and UK frequency databases also suggests that the frequency of individual dropped-in alleles does not mirror the general population frequencies. [ABSTRACT FROM AUTHOR]