An Assessment of the Performance Limitations of the Integrated QuantifilerTM Trio-HRM Assay: A Forensic Tool Designed to Identify Mixtures at the Quantification Stage.

Although guidelines exist for identifying mixtures, these measures often occur at the end-point of analysis and are protracted. To facilitate early mixture detection, we integrated a high-resolution melt (HRM) mixture screening assay into the qPCR step of the forensic workflow, producing the integrated QuantifilerTM Trio-HRM assay. The assay, when coupled with a prediction tool, allowed for 75.0% accurate identification of the contributor status of a sample (single source vs. mixture). To elucidate the limitations of the developed qPCR-HRM assay, developmental validation studies were conducted assessing the reproducibility and samples with varying DNA ratios, contributors, and quality. From this work, it was determined that the integrated QuantifilerTM Trio-HRM assay is capable of accurately identifying mixtures with up to five contributors and mixtures at ratios up to 1:100. Further, the optimal performance concentration range was found to be between 0.025 and 0.5 ng/µL. With these results, evidentiary-like DNA samples were then analyzed, resulting in 100.0% of the mixture samples being accurately identified; furthermore, every time a sample was predicted as a single source, it was true, giving confidence to any single-source calls. Overall, the integrated QuantifilerTM Trio-HRM assay has exhibited an enhanced ability to discern mixture samples from single-source samples at the qPCR stage under commonly observed conditions regardless of the contributor's sex.

Monitoring COVID-19 pandemic in Saudi Arabia using SEIRD model parameters with MEWMA.

BACKGROUND: When the COVID-19 pandemic hit Saudi Arabia, decision-makers were confronted with the difficult task of implementing treatment and disease prevention measures. To make effective decisions, officials must monitor several pandemic attributes simultaneously. Such as spreading rate, which is the number of new cases of a disease compared to existing cases; infection rate refers to how many cases have been reported in the entire population, and the recovery rate, which is how effective treatment is and indicates how many people recover from an illness and the mortality rate is how many deaths there are for every 10,000 people. METHODS: Based on a Susceptible, Exposed, Infected, Recovered Death (SEIRD) model, this study presents a method for monitoring changes in the dynamics of a pandemic. This approach uses a Bayesian paradigm for estimating the parameters at each time using a particle Markov chain Monte Carlo (MCMC) method. The MCMC samples are then analyzed using Multivariate Exponentially Weighted Average (MEWMA) profile monitoring technique, which will "signal" if a change in the SEIRD model parameters change. RESULTS: The method is applied to the pre-vaccine COVID-19 data for Saudi Arabia and the MEWMA process shows changes in parameter profiles which correspond to real world events such as government interventions or changes in behaviour. CONCLUSIONS: The method presented here is a tool that researchers and policy makers can use to monitor pandemics in a real time manner.

A quantifiler™ trio-based HRM screening assay for the accurate prediction of single source versus mixed biological samples.

At present, the forensic DNA workflow is not capable of providing information about the contributor status (single source vs. multiple contributors) of evidentiary samples prior to end-point analysis. This exacerbates the challenges inherent to mixtures and low-template DNA samples. If additional sample information could be provided earlier in the workflow, protocols could be implemented to mitigate these challenges. An integrated Quantiplex®- high resolution melt (HRM) assay was shown to be effective in distinguishing between single source and mixture DNA samples; however, integration of the HRM assay into a more commonly used chemistry would be beneficial to the practitioner community. Thus, the assay was redesigned as an integrated Quantifiler™ Trio-HRM assay, which included the identification of a new DNA-binding dye, an increased reaction volume, and the establishment of new data analysis and standard curve metrics for all targets. This redesigned assay produced quantification values and qualitative values that were comparable to those produced when the same samples were tested using the standard Quantifiler™ Trio chemistry and settings. Further, STR profiles generated with quantification values produced from the integrated Quantifiler™ Trio-HRM assay and standard Quantifiler™ Trio chemistry were complete and fully concordant. Most importantly, the integrated Quantifiler™ Trio-HRM assay was able to accurately predict whether a sample was single source or a mixture 79.2% of the time, demonstrating the potential of this approach. With the incorporation of an expanded training set for prediction modeling, and completion of critical developmental validation studies, this assay could prove useful to the forensic DNA practitioner community.

The impact of vaping ethanol-containing electronic cigarette liquids on roadside impairment investigations.

Legal professionals and others have suggested that vaping electronic cigarettes (e-cigs) prior to or during ethanol breath testing may produce false positives. Preliminary breath tests (PBTs) and evidentiary breath tests (EBTs) measure ethanol in exhaled breath and standardized field sobriety tests (SFSTs) are used to assess impairment. Ethanol has been identified in e-cig liquids (e-liquids). Presented are a series of experiments designed to determine the mechanics of vaping ethanol using an e-cig and the effects of vaping ethanol on the SFSTs and breath tests used by law enforcement officers (LEO). Twelve participants (five females, age: 21-32 and seven males, age: 21-55), vaped either one or ten puffs of an e-liquid (0% or 20% ethanol). LEOs assessed impairment using SFSTs (12 and 42 min), PBTs (<1, 27, 32, 37 and 57 min) and EBTs (2, 29, 34, 39 and 59 min) post-vaping. A self-assessment test was administered post-vaping (22 and 52 min). Baseline responses for all measures were collected prior to vaping. Results demonstrated that ethanol in the e-liquids was aerosolized by e-cigs and produced particles that could reach the deep lung tissue based on mean-mass diameter. Ethanol was detected by PBT <3 min after participants vaped one (0.007-0.030 g/210 L) or ten puffs (013-0.074 g/210 L) of a 20% ethanol e-liquid. Ethanol was not detected by PBT at any subsequent time point. Ethanol was not detected by the EBT under any condition. Impairment was not indicated by the SFST. Some subjective effects were reported, but few statistically significant differences between conditions were indicated. A wait period prior to ethanol breath testing is not always mandated, depending on jurisdiction, or observed in all applications, such as workplace testing. The results demonstrate that a wait period must be employed to prevent vaping-related false-positive breath ethanol results.

Evaluation and Verification of a microRNA Panel Using Quadratic Discriminant Analysis for the Classification of Human Body Fluids in DNA Extracts.

There is significant interest in the use of miRNA analysis for forensic body fluid identification. Demonstrated co-extraction and detection in DNA extracts could make the use of miRNAs a more streamlined molecular body fluid identification method than other RNA-based methods. We previously reported a reverse transcription-quantitative PCR (RT-qPCR) panel of eight miRNAs that classified venous and menstrual blood, feces, urine, saliva, semen, and vaginal secretions using a quadratic discriminant analysis (QDA) model with 93% accuracy in RNA extracts. Herein, miRNA expression in DNA extracts from 50 donors of each body fluid were tested using the model. Initially, a classification rate of 87% was obtained, which increased to 92% when three additional miRNAs were added. Body fluid identification was found to be reliable across population samples of mixed ages, ethnicities, and sex, with 72-98% of the unknown samples classifying correctly. The model was then tested against compromised samples and over biological cycles, where classification accuracy varied, depending on the body fluid. In conclusion, we demonstrated the ability to classify body fluids using miRNA expression from DNA extracts, eliminating the need for RNA extraction, greatly reducing evidentiary sample consumption and processing time in forensic laboratories, but acknowledge that compromised semen and saliva samples can fail to classify properly, and mixed sample classification remains untested and may have limitations.

An illustration of model agnostic explainability methods applied to environmental data.

Historically, two primary criticisms statisticians have of machine learning and deep neural models is their lack of uncertainty quantification and the inability to do inference (i.e., to explain what inputs are important). Explainable AI has developed in the last few years as a sub-discipline of computer science and machine learning to mitigate these concerns (as well as concerns of fairness and transparency in deep modeling). In this article, our focus is on explaining which inputs are important in models for predicting environmental data. In particular, we focus on three general methods for explainability that are model agnostic and thus applicable across a breadth of models without internal explainability: "feature shuffling", "interpretable local surrogates", and "occlusion analysis". We describe particular implementations of each of these and illustrate their use with a variety of models, all applied to the problem of long-lead forecasting monthly soil moisture in the North American corn belt given sea surface temperature anomalies in the Pacific Ocean.

Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the state of Qatar.

During the current COVID-19 pandemic, decision-makers are tasked with implementing and evaluating strategies for both treatment and disease prevention. In order to make effective decisions, they need to simultaneously monitor various attributes of the pandemic such as transmission rate and infection rate for disease prevention, recovery rate which indicates treatment effectiveness as well as the mortality rate and others. This work presents a technique for monitoring the pandemic by employing an Susceptible, Exposed, Infected, Recovered, Death model regularly estimated by an augmented particle Markov chain Monte Carlo scheme in which the posterior distribution samples are monitored via Multivariate Exponentially Weighted Average process monitoring. This is illustrated on the COVID-19 data for the State of Qatar.

Developmental validation of a microRNA panel using quadratic discriminant analysis for the classification of seven forensically relevant body fluids.

Body fluid identification is an important step in the forensic DNA workflow, and more advanced methods, such as microRNA (miRNA) analysis, have been research topics within the community over the last few decades. We previously reported a reverse transcription-quantitative PCR (RT-qPCR) panel of eight miRNAs that could classify blood, menstrual secretions, feces, urine, saliva, semen, and vaginal secretions through analysis of differential gene expression. The purpose of this project was to evaluate this panel in a larger population size, develop a more statistically robust analysis method and perform a series of developmental validation studies. Each of the eight miRNA markers was analyzed in > 40 donors each of blood, menstrual secretions, feces, urine, saliva, semen, and vaginal secretions. A 10-fold cross-validated quadratic discriminant analysis (QDA) model yielded the highest classification accuracy of 93% after eliminating miR-26b and miR-1246 from the panel. Accuracy of body fluid predictions was between 84% and 100% when various population demographics and samples from the same donor over multiple time periods were evaluated, but the assay demonstrated limited scope and reduced accuracy when mixed body fluid samples were tested. Limit of detection was found to be less than 104 copies/µL across multiple commercially available RT-qPCR analysis methods. These data suggest that miR-200b, miR-320c, miR-10b, and miR-891a, when normalized to let-7 g and let-7i, can consistently and robustly classify blood, feces and urine, but additional work is important to improve classification of saliva, semen, and female intimate secretions before implementation in forensic casework.

Safety and Preliminary Efficacy of Lorcaserin for Cocaine Use Disorder: A Phase I Randomized Clinical Trial.

Background and Objectives: Preclinical studies show serotonin (5-HT) 5-HT2C receptor (5-HT2CR) agonists reduce cocaine-seeking and cocaine intake. This study examined safety of the 5-HT2CR agonist lorcaserin administered with cocaine in participants with cocaine use disorder (CocUD). Secondarily, subjective response to cocaine and choice of cocaine vs. money were examined. Methods: A double-blind, randomized, placebo-controlled trial of 25 inpatient non-treatment seeking participants with CocUD. Participants were randomized to either lorcaserin (n = 17) or placebo (n = 8). Primary outcome measures included cardiovascular measures and plasma cocaine levels. Secondary measures of subjective response to cocaine were assessed using a visual analog scale (VAS) and cocaine vs. money progressive ratio choice sessions. Results: Thirteen randomized participants were included in the final analysis. No serious or unexpected adverse events were related to lorcaserin. There were no significant interactions between cocaine and lorcaserin on cardiovascular measures, plasma cocaine, or subjective ratings. After multiple comparisons correction, cocaine significantly increased blood pressure, heart rate, and QTc. Lorcaserin significantly decreased VAS ratings of "feel irritable," "feel hungry," and "I am craving." For the cocaine vs. money choice procedure, there was a significant interaction between choice (cocaine vs. money) and lorcaserin. Participants treated with lorcaserin were more likely to choose cocaine. Discussion and Conclusions: This study showed safety of lorcaserin administered with cocaine but lack of efficacy to reduce the reinforcing effects of cocaine. Scientific Significance: This study is the first to show a disconnect between effects of 5-HT2CR agonists on craving and cocaine choice in human cocaine users.

The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods.

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

Exploring the relationship between white matter integrity, cocaine use and GAD polymorphisms using Bayesian Model Averaging.

Past investigations utilizing diffusion tensor imaging (DTI) have demonstrated that cocaine use disorder (CUD) yields white matter changes, primarily in the corpus callosum. By applying Bayesian model averaging using multiple linear regression in DTI, we demonstrate there may exist relationships between the impaired white matter and glutamic acid decarboxylase (GAD) polymorphisms. This work explored the two-way and three-way interactions between GAD1a (SNP: rs1978340) and GAD1b (SNP: rs769390) polymorphisms and years of cocaine use (YCU). GAD1a was associated with more frontal white matter changes on its own but GAD1b was associated with more midbrain and cerebellar changes as well as a greater increase in white matter changes in the context of chronic cocaine use. The three-way interaction GAD1a|GAD1b|YCU appeared to be roughly an average of the polymorphism two-way interactions GAD1a|YCU and GAD1b|YCU. The three-way interaction demonstrated multiple regions including corpus callosum which featured fewer significant voxel changes, perhaps suggesting a small protective effect of having both polymorphisms on corpus callosum and cerebellar peduncle.

A new method for determining the benchmark dose tolerable region and endpoint probabilities for toxicology experiments.

The increase of exposure to toxic materials and hazardous chemicals is a major concern due to the adverse effect on human health. Among the major concerns of toxicologists is to determine acceptable levels of exposure to hazardous substances. Current approaches often evaluate each endpoint and stressor individually. We propose a novel method to simultaneously determine the Benchmark Dose Tolerable Region (BMDTR) for multiple endpoint and multiple stressor studies by adopting a Bayesian approach. A main concern while assessing the combined toxicological effect of a chemical mixture is the anticipated type of the combined action (i.e. synergistic or antagonistic); thus it was essential to account for interaction effects to handle this situation, imposing more challenges due to the non-linearity of the tolerable region. The proposed method will be evaluated using two approaches, the first one using the estimated value of the posterior median and the second approach using all MCMC samples from the posterior distribution. Furthermore, we propose a new method to determine the endpoint probabilities for each endpoint, which reflects the importance of each endpoint in helping determining the boundaries of the benchmark dose tolerable region (BMDTR).

The Effect of Time and Temperature on the Persistence and Quality of Latent Fingerprints Recovered from 60-Watt Incandescent Light Bulbs.

To understand how time and temperature could affect latent fingerprints, wooden pallets were used to construct five units that each housed ten 60-watt incandescent light bulbs. Fingerprints were deposited on the top, middle, and base of the globes. The bulbs were powered on (except the control bulb in each unit) for 18, 48, 72, 120, 168, 336, 504, and 672 h. Fingerprints recovered from the bulbs by tape lift after black powder processing were given a quality score. A thermal imaging camera determined temperatures on three areas of the bulbs. Fingerprints on the top of the globe (156.3°C) had the lowest quality score, and fingerprints on the middle of the globe (112.6°C) had the highest quality score. The mean quality scores of the three temperature classes were within one standard deviation of one another, meaning there is no way to estimate fingerprint age based on its quality after heat exposure.

A Computational Model for Assessing the Population Health Impact of Introducing a Modified Risk Claim on an Existing Smokeless Tobacco Product.

Computational models are valuable tools for predicting the population effects prior to Food and Drug Administration (FDA) authorization of a modified risk claim on a tobacco product. We have developed and validated a population model using best modeling practices. Our model consists of a Markov compartmental model based on cohorts starting at a defined age and followed up to a specific age accounting for 29 tobacco-use states based on a cohort members transition pathway. The Markov model is coupled with statistical mortality models and excess relative risk ratio estimates to determine survival probabilities from use of smokeless tobacco. Our model estimates the difference in premature deaths prevented by comparing Base Case ("world-as-is") and Modified Case (the most likely outcome given that a modified risk claim is authorized) scenarios. Nationally representative transition probabilities were used for the Base Case. Probabilities of key transitions for the Modified Case were estimated based on a behavioral intentions study in users and nonusers. Our model predicts an estimated 93,000 premature deaths would be avoided over a 60-year period upon authorization of a modified risk claim. Our sensitivity analyses using various reasonable ranges of input parameters do not indicate any scenario under which the net benefit could be offset entirely.

The 5-HT2A Receptor (5-HT2AR) Regulates Impulsive Action and Cocaine Cue Reactivity in Male Sprague-Dawley Rats.

Impulsivity and the attentional orienting response to cocaine-associated cues (cue reactivity) promote relapse in cocaine-use disorder (CUD). A time-dependent escalation of cue reactivity (incubation) occurs during extended, forced abstinence from cocaine self-administration in rats. The investigational serotonin (5-HT) 5-HT2A receptor (5-HT2AR) antagonist/inverse agonist M100907 suppresses impulsive action, or the inability to withhold premature responses, and cocaine-seeking behaviors. The present preclinical study was designed to establish the potential for repurposing the Food and Drug Administration-approved selective 5-HT2AR antagonist/inverse agonist pimavanserin as a therapeutic agent to forestall relapse vulnerability in CUD. In male Sprague-Dawley rats, pimavanserin suppressed impulsive action (premature responses) measured in the 1-choice serial reaction time (1-CSRT) task, similarly to M100907. We also used the 1-CSRT task to establish baseline levels of impulsive action before cocaine self-administration and evaluation of cue reactivity (lever presses reinforced by the discrete cue complex previously paired with cocaine delivery). We observed an incubation of cocaine cue reactivity between day 1 and day 30 of forced abstinence from cocaine self-administration. Baseline levels of impulsive action predicted incubated levels of cocaine cue reactivity in late abstinence. We also found that baseline impulsive action predicted the effectiveness of pimavanserin to suppress incubated cue reactivity in late abstinence from cocaine self-administration at doses that were ineffective in early abstinence. These data suggest that integration of clinical measures of impulsive action may inform refined, personalized pharmacotherapeutic intervention for the treatment of relapse vulnerability in CUD.

Fronto-striatal effective connectivity of working memory in adults with cannabis use disorder.

Previous working memory (WM) studies found that relative to controls, subjects with cannabis use disorder (CUD) showed greater brain activation in some regions (e.g., left [L] and right [R] ventrolateral prefrontal cortex [VLPFC], and L dorsolateral prefrontal cortex [L-DLPFC]), and lower activation in other regions (e.g., R-DLPFC). In this study, effective connectivity (EC) analysis was applied to functional magnetic resonance imaging data acquired from 23 CUD subjects and 23 controls (two groups matched for sociodemographic factors and substance use history) while performing an n-back WM task with interleaved 2-back and 0-back periods. A 2-back minus 0-back modulator was defined to measure the modulatory changes of EC corresponding to the 2-back relative to 0-back conditions. Compared to the controls, the CUD group showed smaller modulatory change in the R-DLPFC to L-caudate pathway, and greater modulatory changes in L-DLPFC to L-caudate, R-DLPFC to R-caudate, and R-VLPFC to L-caudate pathways. Based on previous fMRI studies consistently suggesting that greater brain activations are related to a compensatory mechanism for cannabis neural effects (less regional brain activations), the smaller modulatory change in the R-DLPFC to L-caudate EC may be compensated by the larger modulatory changes in the other prefrontal-striatal ECs in the CUD individuals.

Evaluation of DNA Extraction Methods from Waterlogged Bones: A Pilot Study.

When deaths occur in water, soft tissue decomposes after a temperature-dependent period, making DNA identification dependent on bone. This study examined the effects of water on bone DNA purity and quantity, and determined the best of three extraction methods for isolating DNA. The organic phenol-chloroform method consistently extracted DNA approximating the accepted 260/280 purity value (~1.8); ChargeSwitch® gDNA Plant Kit and DNeasy Blood and Tissue Kit produced fair and unacceptable values, respectively. The purity value for humerus and rib samples was consistent across accumulated degree days (ADD). Significant differences in quantification among extraction methods and between bone types were identified. Ribs and ChargeSwitch® gDNA Plant Kit samples produced the lowest mean Ct values of the bone types and the extraction methods, respectively. Therefore, this study proposes that magnetic bead technology extraction methods and ribs be considered when processing bones that have been submerged in water for any length of time.

A preliminary longitudinal study of white matter alteration in cocaine use disorder subjects.

BACKGROUND: Previous diffusion tensor imaging (DTI) studies have consistently shown that subjects with cocaine use disorder (CocUD) had altered white matter microstructure in the corpus callosum. It is believed that these alterations are due to preexisting factors, chronic cocaine use, or both. However, there is no published longitudinal DTI study on human cocaine users yet which could shed light on the relationship between cocaine use and DTI findings. METHODS: This study used a longitudinal design and DTI to test if the white matter microstructure shows quicker alteration in CocUD subjects than controls. DTI data were acquired from eleven CocUD subjects who participated a treatment study and eleven non-drug-using controls at baseline (Scan 1) and after ten weeks (Scan 2). The baseline fractional anisotropy (FA), a general measure of white matter microstucture, and the change in FA (ΔFA, equals Scan 1 FA minus Scan 2 FA) were both compared between groups. RESULTS: The two groups did not show a difference in FA at baseline. The CocUD subjects had significantly greater ΔFA than the controls in the left splenium of the corpus callosum. In CocUD subjects, greater ΔFA in this region was associated with shorter lifetime cocaine use and greater number of positive cocaine urine samples collected during the treatment. CONCLUSION: The finding in the left splenium is consistent with previous animal studies and provide indirect evidence about the effects of chronic cocaine use on white matter alterations. The subject sample size is small, therefore the results should be treated as preliminary.

Disability and assistance dog implicit association test: a novel IAT.

OBJECTIVE: Individuals with disabilities often face stigma and negative social interactions. Human-animal interaction literature suggests that an individual paired with an animal will be perceived differently than an individual alone. Although people with disabilities report increases in social interactions when with assistance dogs, the reasons for this remain unclear. One possibility is that attitudes toward people with disabilities are altered by the presence of assistance dogs, thus affecting the social behaviors of the perceiver. This study examines whether implicit attitudes toward individuals with disabilities differ in the presence of an assistance dog. METHOD: College students (N = 244) completed the Attitudes of Adults to Dogs scale, an item assessing dog ownership, and the Disabilities and Assistance Dog Implicit Association Test (IAT). RESULTS: A 1-sample t test demonstrated a significant IAT effect, t(240) = 3.62, p < .001, with a positive implicit bias observed toward an individual with a disability when paired with an assistance dog over the individual alone. White individuals were more likely than Black individuals to hold positive implicit attitudes toward an individual with a disability paired with a dog, F(2, 238) = 3.18, p = .04. There were no significant differences in IAT D scores based on gender or dog ownership. IMPLICATIONS: This study extends previous research regarding social interactions for individuals with disabilities who are paired with assistance dogs. Increases in positive implicit attitudes toward an individual with a disability paired with a dog may explain these changes in social interactions. Dogs may serve as a social lubricant, increasing positive social interactions for individuals with disabilities.

Hypothesis testing and power calculations for taxonomic-based human microbiome data.

This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of hypothesis and power/sample size calculations, and applies them for illustration to taxonomic abundance distribution and rank abundance distribution data using HMP Jumpstart data on 24 subjects for saliva, subgingival, and supragingival samples. Software for running these analyses is available.