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Objective: The current study sought to clarify and harness the incremental validity of emotional dysregulation and unawareness (EDU) in emerging adulthood, beyond ADHD symptoms and with respect to concurrent classification of impairment and co-occurring problems, using machine learning techniques. Method: Participants were 1,539 college students (M[subscript age] = 19.5, 69% female) with self-reported ADHD diagnoses from a multisite study who completed questionnaires assessing ADHD symptoms, EDU, and co-occurring problems. Results: Random forest analyses suggested EDU dimensions significantly improved model performance (ps < 0.001) in classifying participants with impairment and internalizing problems versus those without, with the resulting ADHD + EDU classification model demonstrating acceptable to excellent performance (except in classification of Work Impairment) in a distinct sample. Variable importance analyses suggested inattention sum scores and the Limited Access to Emotional Regulation Strategies EDU dimension as the most important features for facilitating model classification. Conclusion: Results provided support for EDU as a key deficit in those with ADHD that, when present, helps explain ADHD's co-occurrence with impairment and internalizing problems. Continued application of machine learning techniques may facilitate actuarial classification of ADHD-related outcomes while also incorporating multiple measures.

This article draws on a digital storytelling approach. The authors describe a pilot study that aimed to activate young learners’ own English language production through collaborative story creation using tablets.

Staphylococcus lugdunensis is a coagulase-negative species responsible for a multitude of infections. These infections often resemble those caused by the more pathogenic staphylococcal species, Staphylococcus aureus, such as skin and soft tissue infections, prosthetic joint infections, and infective endocarditis. Despite a high mortality rate and infections that differ from other coagulase-negative species, little is known regarding S. lugdunensis pathogenesis. The objective of this study is to identify the essential factors for biofilm formation in S. lugdunensis. S. lugdunensis was mutagenized through ethyl methanesulfonate (EMS) exposure, and the individual cells were separated using a cell sorter and examined for biofilm formation at 8 hr and 24 hr timepoints. Mutations that resulted in either increased or decreased biofilm formation were sequenced to identify the genes responsible for the respective phenotypes. A mutation within the S. lugdunensis surface protein A (slsA) gene was common among all of the low biofilm formers, thus suggesting that high expression of this protein is important in biofilm formation. However, other mutations common among the mutants with decreased biofilm formation were in the putative divalent cation transport gene, mgtE. Conversely, a mutation in the gene that codes for the von Willebrand factor binding protein, vwbl, was common among the mutants with increased biofilm formation. Following proteinase K treatment, a significant dispersal of the S. lugdunensis biofilm matrix occurred, thus confirming the presence of primarily protein-mediated biofilms; this is in agreement with previous S. lugdunensis studies. Additionally, all low biofilm formers exhibited decreased protein levels (1.95–2.77 fold change) within the biofilm matrix, while no difference was observed with extracellular DNA (eDNA) or polysaccharides. This study presents a unique methodology to identify genes that affect biofilm formation and sheds light on S. lugdunensis pathogenesis.

A straightforward experimental set-up, requiring a two-liter bottle, a ruler and a stopwatch, is used to provide data appropriate for modelling with Torricelli's Law in the simplest case, and a more sophisticated differential equation when losses are taken into account and a pipe extension is considered. With only an exit hole included in the model, the governing equation can be integrated analytically to find the liquid height in the tank as a function of time. In the more complicated instance, the governing equation requires numerical integration. In both cases, a discharge coefficient can be employed as a fitting parameter. Excellent agreement between the experimental data and the theoretical modelling results has been obtained. An analytical solution with comparable accuracy to the numerical version is also possible for a pipe extension under the operating conditions considered here. Student reception of the experiment was generally positive, although some nuances were not fully grasped.

Abstract The analysis of vascular morphology and functionality enables the assessment of disease activity and therapeutic effects in various pathologies. Raster‐scanning optoacoustic mesoscopy (RSOM) is an imaging modality that enables the visualization of superficial vascular networks in vivo. In murine models of colitis, deep vascular networks in the colon wall can be visualized by transrectal absorber guide raster‐scanning optoacoustic mesoscopy (TAG‐RSOM). In order to accelerate the implementation of this technology in translational studies of inflammatory bowel disease, an image‐processing pipeline for TAG‐RSOM data has been developed. Using optoacoustic data from a murine model of chemically‐induced colitis, different image segmentation methods are compared for visualization and quantification of deep vascular patterns in terms of vascular network length and complexity, blood volume, and vessel diameter. The presented image‐processing pipeline for TAG‐RSOM enables label‐free in vivo assessment of changes in the vascular network in murine colitis with broad applications for inflammatory bowel disease research.