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Current analysis of forensic DNA stains relies on the probabilistic interpretation of bulk-processed samples that represent mixed profiles consisting of an unknown number of potentially partial representations of each contributor. Single-cell methods, in contrast, offer a solution to the forensic DNA mixture problem by incorporating a step that separates cells before extraction. A forensically relevant single-cell pipeline relies on efficient direct-to-PCR extractions that are compatible with standard down- stream forensic reagents. Here we demonstrate the feasibility of implementing single-cell pipelines into the forensic process by exploring four metrics of electropherogram (EPG) signal quality—i.e., allele detection rates, peak heights, peak height ratios, and peak height balance across low- to high-molecular-weight short tandem repeat (STR) markers—obtained with four direct-to-PCR extraction treatments and a common post-PCR laboratory procedure. Each treatment was used to extract DNA from 102 single buccal cells, whereupon the amplification reagents were immediately added to the tube and the DNA was amplified/injected using post-PCR conditions known to elicit a limit of detection (LoD) of one DNA molecule. The results show that most cells, regardless of extraction treatment, rendered EPGs with at least a 50% true positive allele detection rate and that allele drop-out was not cell independent. Statistical tests demonstrated that extraction treatments significantly impacted all metrics of EPG quality, where the Arcturus® PicoPure™ extraction method resulted in the lowest median allele drop-out rate, highest median average peak height, highest median average peak height ratio, and least negative median values of EPG sloping for GlobalFiler™ STR loci amplified at half volume. We, therefore, conclude the feasibility of implementing single-cell pipelines for casework purposes and demonstrate that inferential systems assuming cell independence will not be appropriate in the probabilistic interpretation of a collection of single-cell EPGs.

Interpreting forensic DNA signal is arduous since the total intensity is a cacophony of signal from noise, artifact, and allele from an unknown number of contributors (NOC). An alternate to traditional bulk- processing pipelines is a single-cell one, where the sample is collected, and each cell is sequestered resulting in n single-source, single-cell EPGs (scEPG) that must be interpreted using applicable strategies. As with all forensic DNA interpretation strategies, high quality electropherograms are required; thus, to enhance the credibility of single-cell forensics, it is necessary to produce an efficient direct-to- PCR treatment that is compatible with prevailing downstream labo-ratory processes.We incorporated the semi-automated micro-fluidic DEPArray™ technology into the single-cell laboratory and optimized its implementation by testing the effects of four laboratory treatments on single-cell profiles. We focused on testing effects of phosphate buffer saline (PBS) since it is an important reagent that mitigates cell rup-ture but is also a PCR inhibitor. Specifically, we explored the effect of decreasing PBS concentrations on five electropherogram-quality metrics from 241 leukocytes: profile drop- out, allele drop-out, allele peak heights, peak height ratios, and scEPG sloping. In an effort to improve reagent use, we also assessed two concentrations of proteinase K. The results indicate that decreasing PBS concentrations to 0.5X or 0.25X improves scEPG quality, while modest modifications to proteinase K concentrations did not significantly impact it. We, therefore, conclude that a lower than recommended pro-teinase K concentration coupled with a lower than recommended PBS concentration results in enhanced scEPGs within the semi-automated single-cell pipeline.

Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpretation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational tools that make point estimates on the Number of Contributors (NOC) to a sample have been developed, as have Bayesian methods that evaluate an A Posteriori Probability (APP) distribution on the NOC. In cases where an overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the evidence is evaluated with an inadequate set of propositions. In the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the NOC given an electropherogram, by reporting on an implementation where the user can add assumed contributors. NOCIt is a continuous system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele frequencies in a reference population. When conditioned on a known contributor, we found that the mode of the APP distribution can shift to one greater when compared with the circumstance where no known contributor is assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture. In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution and defense hypotheses, we show that this computation must not only use coincident models, but also coincident contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts.

Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signal from forensic stains is often garnered from low-copy, high-contributor-number samples and is frequently obfuscated by allele sharing, allele drop-out, stutter and noise. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous, probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution’s and defense’s hypotheses have been developed. In the current paper, we validate a new version of the NOCIt inference platform that determines an A Posteriori Probability (APP) distribution of the number of contributors given an electropherogram. NOCIt is a continuous inference system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise and allelic drop-out while taking into account allele frequencies in a reference population. We established the algorithm’s performance by conducting tests on samples that were representative of types often encountered in practice. In total, we tested NOCIt’s performance on 815 degraded, UV-damaged, inhibited, differentially degraded, or uncompromised DNA mixture samples containing up to 5 contributors. We found that the model makes accurate, repeatable and reliable inferences about the NOCs and significantly outperformed methods that rely on signal filtering. By leveraging recent theoretical results of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establish the NOC can be treated as a nuisance variable, we demonstrated that when NOCIt’s APP is used in conjunction with a downstream likelihood ratio (LR) inference system that employs the same probabilistic model, a full evaluation across multiple contributor numbers is rendered. This work, therefore, illustrates the power of modern probabilistic systems to report holistic and interpretable weights-of-evidence to the trier-of-fact without assigning a specified number of contributors or filtering signal.

DNA-based human identity testing is conducted by comparison of PCR-amplified polymorphic Short Tandem Repeat (STR) motifs from a known source with the STR profiles obtained from uncertain sources. Samples such as those found at crime scenes often result in signal that is a composite of incomplete STR profiles from an unknown number of unknown contributors, making interpretation an arduous task. To facilitate advancement in STR interpretation challenges we provide over 25,000 multiplex STR profiles produced from one to five known individuals at target levels ranging from one to 160 copies of DNA. The data, generated under 144 laboratory conditions, are classified by total copy number and contributor proportions. For the 70% of samples that were synthetically compromised, we report the level of DNA damage using quantitative and end-point PCR. In addition, we characterize the complexity of the signal by exploring the number of detected alleles in each profile.

Short tandem repeat (STR) profiling from DNA samples has long been the bedrock of human identification. The laboratory process is composed of multiple procedures that include quantification, sample dilution, PCR, electrophoresis, and fragment analysis. The end product is a short tandem repeat electropherogram comprised of signal from allele, artifacts, and instrument noise. In order to optimize or alter laboratory protocols, a large number of validation samples must be created at significant expense. As a tool to support that process and to enable the exploration of complex scenarios without costly sample creation, a mechanistic stochastic model that incorporates each of the aforementioned processing features is described herein. The model allows rapid in silico simulation of electropherograms from multicontributor samples and enables detailed investigations of involved scenarios. An implementation of the model that is parameterized by extensive laboratory data is publically available. To illustrate its utility, the model was employed in order to evaluate the effects of sample dilutions, injection time, and cycle number on peak height, and the nature of stutter ratios at low template. We verify the model's findings by comparison with experimentally generated data.

Continuous mixture interpretation methods that employ probabilistic genotyping to compute the Likelihood Ratio (LR) utilize more information than threshold-based systems. The continuous interpretation schemes described in the literature, however, do not all use the same underlying probabilistic model and standards outlining which probabilistic models may or may not be implemented into casework do not exist; thus, it is the individual forensic laboratory or expert that decides which model and corresponding software program to implement. For countries, such as the United States, with an adversarial legal system, one can envision a scenario where two probabilistic models are used to present the weight of evidence, and two LRs are presented by two experts. Conversely, if no independent review of the evidence is requested, one expert using one model may present one LR as there is no standard or guideline requiring the uncertainty in the LR estimate be presented. The choice of model determines the underlying probability calculation, and changes to it can result in non-negligible differences in the reported LR or corresponding verbal categorization presented to the trier-of-fact. In this paper, we study the impact of model differences on the LR and on the corresponding verbal expression computed using four variants of a continuous mixture interpretation method. The four models were tested five times each on 101, 1-, 2- and 3-person experimental samples with known contributors. For each sample, LRs were computed using the known contributor as the person of interest. In all four models, intra-model variability increased with an increase in the number of contributors and with a decrease in the contributor's template mass. Inter-model variability in the associated verbal expression of the LR was observed in 32 of the 195 LRs used for comparison. Moreover, in 11 of these profiles there was a change from LR > 1 to LR < 1. These results indicate that modifications to existing continuous models do have the potential to significantly impact the final statistic, justifying the continuation of broad-based, large-scale, independent studies to quantify the limits of reliability and variability of existing forensically relevant systems.

Samples containing low-copy numbers of DNA are routinely encountered in casework. The signal acquired from these sample types can be difficult to interpret as they do not always contain all of the genotypic information from each contributor, where the loss of genetic information is associated with sampling and detection effects. The present work focuses on developing a validation scheme to aid in mitigating the effects of the latter. We establish a scheme designed to simultaneously improve signal resolution and detection rates without costly large-scale experimental validation studies by applying a combined simulation and experimental based approach. Specifically, we parameterize an in silico DNA pipeline with experimental data acquired from the laboratory and use this to evaluate multifarious scenarios in a cost-effective manner. Metrics such as signal1copy-to-noise resolution, false positive and false negative signal detection rates are used to select tenable laboratory parameters that result in high-fidelity signal in the single-copy regime. We demonstrate that the metrics acquired from simulation are consistent with experimental data obtained from two capillary electrophoresis platforms and various injection parameters. Once good resolution is obtained, analytical thresholds can be determined using detection error tradeoff analysis, if necessary. Decreasing the limit of detection of the forensic process to one copy of DNA is a powerful mechanism by which to increase the information content on minor components of a mixture, which is particularly important for probabilistic system inference. If the forensic pipeline is engineered such that high-fidelity electropherogram signal is obtained, then the likelihood ratio (LR) of a true contributor increases and the probability that the LR of a randomly chosen person is greater than one decreases. This is, potentially, the first step towards standardization of the analytical pipeline across operational laboratories.

Preparation and performance of bimetallic Ni (1− x ) Co x -YSZ and Ni (1− x ) Cu x -YSZ anodes were tested to overcome common deficiencies of carbon and sulfur poisoning in SOFCs. Ni 1− x Co x O-YSZ and Ni (1− x ) Cu x O-YSZ precursors were synthesized via co-precipitation of their respective chlorides. Single cell solid oxide fuel cells of these bimetallic anodes were tested in H 2 , CH 4 , and H 2 S/CH 4 fuel mixtures. Addition of Cu 2+ into the NiO lattice resulted in large metal particle sizes and decreased SOFC performance. Addition of Co 2+ into the NiO lattice to form Ni 0.92 Co 0.08 O-YSZ anode precursor produced a cermet with a large BET surface area and active metal surface area, thus increasing the rate of hydrogen oxidation for this sample. The performance of both bimetallics was found to quickly degrade in dry CH 4 due to carbon deposition and lifting of the anode from the electrolyte. However, Ni 0.69 Co 0.31 -YSZ showed superior activity in a 10% (v/v) H 2 S/CH 4 fuel mixture, surpassing performance with H 2 fuel, thereby demonstrating the exciting prospect of using sulfidated Ni (1− x ) Co x -YSZ as SOFC anodes in sulfur containing methane streams. The active anode becomes a sulfidated alloy (Ni-Co-S) under operating conditions. This anode showed enhanced performance, which surpassed those of sulfidated Ni and Co anodes, thereby suggesting a synergistic behaviour in the Ni-Co-S anode.

Single cell solid oxide fuel cells using Ni-YSZ and Co-YSZ anodes were tested in H2, CH4, H2S/H2 and H2S/CH4 fuel mixtures. Their performance was found to quickly degrade in dry CH4 due to carbon deposition and lifting of the anode from the electrolyte. In contrast, hydrogen or methane containing H2S showed an increase in exchange current densities when compared to H2,M/YSZ/LSM,air systems (M = Ni or Co) despite having less optimal anode microstructure. Conversion from metal to metal-sulfide in the presence of H2S produced large, dense metal-sulfide particles surrounded by YSZ, thus decreasing the triple-phase boundary. Furthermore, CoS-based anodes showed phase segregation and densification toward the electrolyte. Despite this, long-term testing at ηa = 0.5 V of the H2S/CH4,CoS-YSZ/YSZ/LSM,air system showed no signs of degradation of the anode over a 6-day period. Only after removal of H2S from the H2S/CH4 stream did the CoS-anode reduce back to Co, signifying H2S is required to maintain the metal-sulfide active anode.

The catalytic oxidation of methane was examined over 5% (w/w) NiO- on CeO2 or ZrO2. The particle size of single components was varied independently to investigate the effect of metal and support particle size on activity for complete methane oxidation. Catalyst activity is most affected by NiO particle size, however the support particle size also plays an active role. Results show that the particle size of ceria has a greater effect on activity than that of zirconia. This is attributed to ceria's greater oxygen transport capacity and contribution to the reaction mechanism. Changes in ceria particle size significantly affected the Arrhenius parameters, suggesting ceria forms part of the active site on a ceria-supported NiO catalyst. In contrast, zirconia particle size did not result in changes in activation energy, but affected only the number of catalytic sites as seen by changes in the pre-exponential factor. Ceria was also found to reduce deactivation of the catalyst due to increased oxygen transport to the active sites during reaction.

In forensic DNA interpretation, the likelihood ratio (LR) is often used to convey the strength of a match. Expanding on binary and semi-continuous methods that do not use all of the quantitative data contained in an electropherogram, fully continuous methods to calculate the LR have been created. These fully continuous methods utilize all of the information captured in the electropherogram, including the peak heights. Recently, methods that calculate the distribution of the LR using semi-continuous methods have also been developed. The LR distribution has been proposed as a way of studying the robustness of the LR, which varies depending on the probabilistic model used for its calculation. For example, the LR distribution can be used to calculate the p-value, which is the probability that a randomly chosen individual results in a LR greater than the LR obtained from the person-of-interest (POI). Hence, the p-value is a statistic that is different from, but related to, the LR; and it may be interpreted as the false positive rate resulting from a binary hypothesis test between the prosecution and defense hypotheses. Here, we present CEESIt, a method that combines the twin features of a fully continuous model to calculate the LR and its distribution, conditioned on the defense hypothesis, along with an associated p-value. CEESIt incorporates dropout, noise and stutter (reverse and forward) in its calculation. As calibration data, CEESIt uses single source samples with known genotypes and calculates a LR for a specified POI on a question sample, along with the LR distribution and a p-value. The method was tested on 303 files representing 1-, 2- and 3-person samples injected using three injection times containing between 0.016 and 1 ng of template DNA. Our data allows us to evaluate changes in the LR and p-value with respect to the complexity of the sample and to facilitate discussions regarding complex DNA mixture interpretation. We observed that the amount of template DNA from the contributor impacted the LR - small LRs resulted from contributors with low template masses. Moreover, as expected, we observed a decrease of p-values as the LR increased. A p-value of 10-9 or lower was achieved in all the cases where the LR was greater than 108. We tested the repeatability of CEESIt by running all samples in duplicate and found the results to be repeatable. Refereed/Peer-reviewed

There are three dominant contributing factors that distort short tandem repeat profile measurements, two of which, stutter and variations in the allelic peak heights, have been described extensively. Here we characterise the remaining component, baseline noise. A probabilistic characterisation of the non-allelic noise peaks is not only inherently useful for statistical inference but is also significant for establishing a detection threshold. We do this by analysing the data from 643 single person profiles for the Identifiler Plus kit and 303 for the PowerPlex 16 HS kit. This investigation reveals that although the dye colour is a significant factor, it is not sufficient to have a per-dye colour description of the noise. Furthermore, we show that at a per-locus basis, out of the Gaussian, log-normal, and gamma distribution classes, baseline noise is best described by log-normal distributions and provide a methodology for setting an analytical threshold based on that deduction. In the PowerPlex 16 HS kit, we observe evidence of significant stutter at two repeat units shorter than the allelic peak, which has implications for the definition of baseline noise and signal interpretation. In general, the DNA input mass has an influence on the noise distribution. Thus, it is advisable to study noise and, consequently, to infer quantities like the analytical threshold from data with a DNA input mass comparable to the DNA input mass of the samples to be analysed.

A reduced mesoporous 5% Pd-YSZ precatalyst was observed to be very active for the Heck reaction providing up to 770,000 TON. Three-phase tests have experimentally confirmed that a leached homogeneous species is responsible for the activity. Residual Pd concentration in solution is minimal (

During the course of conventional testing of CODIS standards at the Alabama Department of Forensic Sciences, a sample with a heterozygous null genotype at D13S317 was discovered using the PowerPlex® 1.1 kit (Promega, Madison, WI). The loss of both alleles was confirmed when the sample was amplified using PowerPlex® 1.2 primers and resulted in a 9, 10 genotype at this locus. To determine the cause of the silent alleles, the ADFS designed D13S317 primers which encompassed the PowerPlex 1.1 D13S317 primer binding sites and sequenced the region. Both alleles showed the presence of two substitutions (T → A and G → T) at positions 1 and 5 (5′ → 3′) of the reverse primer (positions 196 and 200 of the sequence in GenBank accession number G09017 ). Since the mutations were identical on both alleles, they may be assumed to be of ancestral origin.

Nickel oxide yttria-stabilized zirconia (NiO–YSZ) anode materials were synthesized via hydrolysis of the corresponding chloride solutions with NH3, NH3+NaOH, and NaOH as precipitation agents. Powder properties such as crystallite size, morphology, and sintering behavior of the final NiO–YSZ materials were also studied. The mechanism of the formation of NiO–YSZ was established for the different co-precipitation techniques by the direct observation of Ni(NH3)n+2 complexes, Ni(OH)2 and NiO at different stages of the synthesis process. A direct relationship between the precipitation agent, the order of calcination from dry sample to final product, the final composition, the crystallite sizes and particle sizes of NiO, and the sinterability of the final products was established. A comparison of the powder and individual component properties indicate that the choice of precipitation agent greatly influences the final characteristics. Ni/YSZ materials prepared by NH3+NaOH precipitation offer higher Ni dispersion and nanocrystallinity of both the Ni and YSZ phases. The conductivity of both prepared materials compares well with mixed-oxide materials of higher Ni content.

Phase pure, dual phase NiO-YSZ and CuO-YSZ have been synthesized by the co-precipitation method. Sample systems involving the synthesis of these materials at various pH levels, and at varying Ni, Cu and yttria content have been described. The nickel system shows a relatively simple precipitation pathway in which Ni 2+ precipitates out of solution as Ni(OH) 2 . In contrast, Cu 2+ precipitates as Cu 2 (OH) 3 Cl, followed by a conversion to CuO with increasing pH. Phase separation of monoclinic zirconia can be avoided by an appropriate choice of metal to base concentration ratios during precipitation or by the addition of additional yttria. The effect of the different preparations on conductivity suggests a small effect on short term performance. Large increases in conductivity and lower temperatures required during methane adsorption for Cu-YSZ make copper an attractive alternative to nickel for SOFC applications once optimal precipitation conditions have been established.

For forensic purposes, short tandem repeat allele signals are used as DNA fingerprints. The interpretation of signals measured from samples has traditionally been conducted by applying thresholding. More quantitative approaches have recently been developed, but not for the purposes of identifying an appropriate signal model. By analyzing data from 643 single person samples, we develop such a signal model. Three standard classes of two-parameter distributions, one symmetric (normal) and two right-skewed (gamma and log-normal), were investigated for their ability to adequately describe the data. Our analysis suggests that additive noise is well modeled via the log-normal distribution class and that variability in peak heights is well described by the gamma distribution class. This is a crucial step towards the development of principled techniques for mixed sample signal deconvolution., United States. Dept. of Justice. National Institute of Justice (2012-DN-BX-K050)