Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

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.

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.

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.

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.

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).

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.

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.

Effects of age and composition of field-produced biofilms on oyster larval setting.

Lack of success in restoring the native Eastern oyster, Crassostrea virginica, to Chesapeake Bay has been linked to the low occurrence of oyster larval setting in tributaries to the Bay. Among the many potential factors that could affect efforts to produce oysters through aquaculture or supplementation of shell beds is substratum condition. The present study examined larval setting on field-produced biofilms from Little Wicomico River (Virginia, USA) to assess whether bacterial community structure (examined by terminal restriction fragment length polymorphism, T-RFLP) or other characteristics of contemporary biofilms in this tributary (biofilm age and mass, algal abundance, and percentage organic matter) inhibited setting of larval oysters. The structure of the natural and heterogenous bacterial community in the biofilms and the success of oyster set were correlated, suggesting that specific microbial species may play a role in oyster setting. Larval set increased with biofilm age and mass, suggesting that established field-produced biofilms have no inhibitory effect. In contrast, the percentage of organic matter was negatively correlated with oyster set, whereas chlorophyll a concentration had no observed effect. The study expands prior knowledge by providing a more realistic timeframe for biofilm development (weeks as opposed to days), recounting effects of biofilms that are more representative of the natural dynamic and disturbance processes that would be expected to occur on submerged structures, and by incorporating seasonal and spatial variation. An important negative effect observed during the study period was heavy predation by Stylochus ellipticus on newly set oysters. Overall, the results of this study, which is the first assessment of the effects of biofilms produced naturally within a Chesapeake Bay tributary, suggest that the absence of large numbers of oysters in Little Wicomico River is not related to microbes or other specific characteristics of biofilms that develop on suitable setting substrata, but rather to heavy predation of newly set larvae.

Longitudinal changes in strength of police officers with gender comparisons.

Strength is a critical factor in the health and job performance of police officers. Using a retrospective longitudinal design, the purpose of this study was to identify differences in strength scores from initial recruitment to in-service tests and to compare gender differences. Strength changes were also compared in low- to high-strength groups. Strength scores included bench press, bench press/lean weight, and bench press/weight. Body weight, percent body fat, and bench press scores were retrieved for the 1990-1995 recruit classes and were paired to most recent scores on 2006 in-service fitness record. Sample included 327 police officers: 30 females and 297 males. Mean age at initial recruitment was 24.6 years and for in-service was 37.1 years. Average time between tests was 12.5 years. Over this period bench press and bench press/lean weight significantly increased for both gender groups (p

dcDegenerate oligonucleotide primed-PCR for multilocus, genome-wide analysis from limited quantities of DNA.

This study modified the degenerate oligonucleotide primed-polymerase chain reaction (DOP-PCR)-based whole genome amplification method for improvement of downstream genome-wide analysis of low copy number DNA samples ( 0.50 intralocus heterozygote peak ratios were observed for most DNA input quantities examined. These results show that modifications of the traditional DOP-PCR reaction (dcDOP-PCR) to include the use of a more degenerate primer (10 N), 12 nonspecific cycles, and a proofreading enzyme allows for a more complete, balanced chromosome amplification from limited and/or compromised clinical and biological samples.

Physical activity, weight gain and occupational health among call centre employees.

BACKGROUND: A need exists to address ergonomic, weight gain and obesity risks in sedentary occupations. AIM: To determine relationships between body mass index (BMI), weight gain, ergonomic and exercise variables in sedentary workers. METHODS: An anonymous questionnaire was administered regarding body weight, height, weight gained since employment, body part discomfort, shift fatigue, time to achieve job adaptation, physical activity, fitness centre membership, previous employment type and previous injury. RESULTS: Subjects were 393 volunteers (mean age 34 years, 71% female) employed in a call centre. Sixty-eight per cent of participants gained weight averaging 0.9 kg/month for 8 months. Significant findings (P < 0.05) were as follows: non-obese individuals gained less weight than obese individuals, fitness club members had higher BMIs and weight gains than non-members, previously injured individuals gained more weight than non-injured individuals, non-weight gainers reported higher metabolic equivalent-min/week expenditure in relation to vigorous exercise. CONCLUSIONS: Participants reported substantial weight gain over a period of 8 months. In contrast to walking and moderate exercise, only vigorous exercise was significantly associated with non-weight gain. Three risk factors were identified for weight gain: obese when hired, history of previous injury and lack of vigorous exercise.