Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance.

The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available. A metabarcoding approach is often used to predict sample provenance, through the extraction and comparison of the DNA signatures found within different environmental materials, such as the bacteria within soil or fungi within dust. Such approaches are combined with bioinformatics workflows and statistical modelling, often as part of large-scale study, with less emphasis on the investigation of the adaptation of these methods to a smaller scale method for forensic use. The present work was investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs. This adaptation was to facilitate a standardized method for consistent, reproducible sample treatment, including bioinformatics processing and final application of resulting data to the available prediction model. To investigate this small-scale method, 76 DNA samples were treated as anonymous test samples and analyzed using the standardized process to demonstrate and evaluate processing and customized sequence data analysis. This testing included samples originating from countries previously used to train the model, samples artificially mixed to represent multiple or mixed countries, as well as outgroup samples. Positive controls were also developed to monitor laboratory processing and bioinformatics analysis. Through this evaluation we were able to demonstrate that the samples could be processed and analyzed in a consistent manner, facilitated by a relatively user-friendly bioinformatic pipeline for sequence data analysis. Such investigation into standardized analyses and application of metabarcoding data is of key importance for the future use of applied microbiology in forensic science.

Mindfulness-based interventions with youth: A comprehensive meta-analysis of group-design studies.

The treatment effects of Mindfulness-Based Interventions (MBIs) with youth were synthesized from 76 studies involving 6121 participants. A total of 885 effect sizes were aggregated using meta-regression with robust variance estimation. Overall, MBIs were associated with small treatment effects in studies using pre-post (g=0.305, SE=0.039) and controlled designs (g=0.322, SE=0.040). Treatment effects were measured after a follow-up period in 24 studies (n=1963). Results demonstrated that treatment effects were larger at follow-up than post-treatment in pre-post (g=0.462, SE=0.118) and controlled designs (g=0.402, SE=0.081). Moderator analyses indicated that intervention setting and intervention dosage were not meaningfully related to outcomes after controlling for study design quality. With that said, the between-study heterogeneity in the intercept-only models was consistently small, thus limiting the amount of variance for the moderators to explain. A series of exploratory analyses were used to investigate the differential effectiveness of MBIs across four therapeutic process domains and seven therapeutic outcome domains. Small, positive results were generally observed across the process and outcome domains. Notably, MBIs were associated with moderate effects on the process variable of mindfulness in controlled studies (n=1108, g=0.510). Limitations and directions for future research and practice are discussed.