With exposure emerging as a key ingredient in anxiety treatment for childhood anxiety disorders (CADs), expansion of exposure techniques is a promising avenue for improving treatment efficacy. The present study examined use of imaginal exposure (IE), a technique understudied in the treatment of CADs. Specifically, the study tested whether two forms of exposure to worries (verbal IE and virtual reality exposure therapy, VRET) would be effective and acceptable forms of exposure with youth. Twenty youth with fears of academic failure completed both types of worry exposure, presented in randomized order. Regardless of order of presentation, both verbal IE and VRET elicited moderate anxiety that decreased to mild over the span of the exposures. Both were found to be acceptable by youth and neither was associated with negative side effects. Youth found VRET to be slightly more interesting and novel, but noted that verbal IE was more realistic and individualized. The present study supports the use of standalone worry exposure as an effective and acceptable treatment for general worries in youth and suggests VRET could be more effective with improved realism.The purpose of the present study was to propose and test two models to understand the relationship between perceived vulnerability to COVID-19 (PVC) and COVID-19-related traumatic stress (TS), as well as the variables that may mediate and moderate this relationship among individuals who have not yet been infected with COVID-19. Using an online survey, data were collected between late March and early April 2020. Nintedanib concentration Participants were recruited through Amazon Mechanical Turk and included 747 adults living in the United States. Supporting our hypotheses, results indicated that both COVID-19-related worries and social isolation were significant mediators of the relationship between PVC and TS (Model 1). In addition, the results of a moderated mediation analysis indicated that the indirect effect of PVC on TS through COVID-19-related worries was stronger for participants who reported greater social isolation (Model 2). Although future research is needed, these findings suggest that both social isolation and disease-related worries may be important variables that can be targeted in interventions to reduce pandemic-related TS.Bone morphogenetic protein 7 (BMP7) is of the BMP subfamily, and has effects on female fertility by regulating steroidogenesis, granulosa cell states, and follicular development. In the present study, there was assessment of the combined genotypes formed by the three variants within the 3′-UTR of BMP7 gene as associations with sow reproductive functions. The 3′-UTR of the BMP7 gene of pigs was identified using the 3′ RACE assay, and its full-length sequence was found to be 1538 bp in length. Multiple RNA regulatory elements were detected in this region, luciferase activity assays were performed and results indicated miR-22-3p affects BMP7 by directly binding to the miRNA response element in the 3′-UTR (c.2358-2382). In addition, two novel complete linkage variants, c.2256 G > C and a 7-bp indel (c.2259-2265), were identified within the 3′-UTR of the BMP7 gene of pigs. Importantly, combined genotypes with these two novel variants and c.1569A > G, a variant previously identified in the BMP7 3′-UTR of pigs, were associated with sow reproductive traits, including the total number of piglets born, number of dead piglets at birth, and litter weight in the Yorkshire pig population studies. Results from the present study confirm that BMP7 is a candidate gene for the reproductive traits in Yorkshire sows.In casework there are often multiple mixed DNA profiles with a single unknown offender who is believed to be a contributor. Sometimes none of these profiles are individually informative enough to evaluate whether a person of interest (PoI) is a contributor. We propose a method that combines evidence across multiple mixtures to better resolve the genotype of a queried common donor. This method can also resolve genotypes of a queried non-common donor (i.e. unique to one profile), when other donors of that mixture have contributed to multiple samples. The approach has similarities with the analysis of replicate PCRs, with the difference being that we do not necessarily assume that all the contributors are the same across the evidential profiles, nor do we assume that parameters such as the mixture ratio are constant. Our method can be used to compute an LR for a PoI being the common contributor to multiple stains. It is also possible to interrogate a database of reference profiles to search for the queried donor (common or non-common). We show that the method can identify a queried contributor in cases where individual comparisons have limited capacity to discriminate between donors and non-donors, when some assumption(s) can be made about multiple contributors being from the same sources (queried or not).This study describes a multi-laboratory validation of DNAxs, a DNA eXpert System for the data management and probabilistic interpretation of DNA profiles [1], and its statistical library DNAStatistX to which, besides the organising laboratory, four laboratories participated. The software was modified to read multiple data formats and the study was performed prior to the release of the software to the forensic community. The first exercise explored all main functionalities of DNAxs with feedback on user-friendliness, installation and general performance. Next, every laboratory performed likelihood ratio (LR) calculations using their own dataset and a dataset provided by the organising laboratory. The organising laboratory performed LR calculations using all datasets. The datasets were generated with different STR typing kits or analysis systems and consisted of samples varying in DNA amounts, mixture ratios, number of contributors and drop-out level. Hypothesis sets had the correct, under- and over-assigned number of contributors and true and false donors as person of interest. When comparing the results between laboratories, the LRs were foremost within one unit on log10 scale. The few LR results that deviated more had differences for the parameters estimated by the optimizer within DNAStatistX. Some of these were indicated by failed iteration results, others by a failed model validation, since unrealistic hypotheses were included. When these results that do not meet the quality criteria were excluded, as is in accordance with interpretation guidelines, none of the analyses in the different laboratories yielded a different statement in the casework report. Nonetheless, changes in software parameters were sought that minimized differences in outcomes, which made the DNAStatistX module more robust. Overall, the software was found intuitive, user-friendly and valid for use in multiple laboratories.