Involuntary Musical Imagery (INMI) or "earworms" describes the experience whereby a tune comes into the mind and repeats without conscious control. The present article uses an inductive, generative, grounded theory-based qualitative analysis to classify reports of everyday INMI circumstances, and creates graphical models that determine their relative frequency within two population samples; listeners to the BBC radio station 6 Music and an online survey. Within the two models, four abstract categories were defined that described the characteristics of the circumstances surrounding the onset of INMI episodes; Music exposure, Memory triggers, Affective states, and Low attention states respectively. We also note the variety of musical media by which exposure to a tune results in an INMI episode and discuss the impact of musical engagement on INMI experiences. The findings of the present study are considered within a framework of involuntary retrieval theory from both the autobiographical and semantic memory literatures. In addition, the results highlight the potential facilitative effects of varying affective and attentional states on INMI episodes. Keywords affective states, attention states, everyday music listening, involuntary autobiographical/semantic memory (IAM/ISM), involuntary musical imagery (INMI)

INVOLUNTARY MUSICAL IMAGERY (INMI) DESCRIBESthe everyday phenomenon of having a tune stuck in the head. Research has established the ubiquity of this form of spontaneous cognition but the predictive role of individual differences is still debated. This study examines the impact of everyday musical behaviors and subclinical obsessive compulsive attributes on INMI experiences. In total 1,536 participants completed three online questionnaires; a novel inventory of musical behavior and INMI, and a standardized obsessive compulsion (OC) inventory. Exploratory factor analysis (N ¼ 512) and structural equation modelling (N ¼ 1,024) were applied. Everyday singing and music listening positively predict length and frequency of reported INMI episodes, respectively. No relationships were found with musical training. High OC was positively related to INMI frequency and disturbance, but only indirectly to INMI episode length and unpleasantness. The identified contributory factors of INMI experiences are discussed in the context of musical memory and spontaneous mental activity.

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Summary Exitron splicing is a type of alternative splicing where coding sequences are spliced out. Recently, exitron splicing has been shown to increase proteome plasticity and play a role in cancer. Long-read RNA-seq is well suited for quantification and discovery of alternative splicing events; however, there are currently no tools available for detection and annotation of exitrons in long-read RNA-seq data. Here we present ScanExitronLR, an application for the characterization and quantification of exitron splicing events in long-reads. From a BAM alignment file, reference genome and reference gene annotation, ScanExitronLR outputs exitron events at the individual transcript level. Outputs of ScanExitronLR can be used in downstream analyses of differential exitron splicing. In addition, ScanExitronLR optionally reports exitron annotations such as truncation or frameshift type, nonsense-mediated decay status, and Pfam domain interruptions. We demonstrate that ScanExitronLR performs better on noisy long-reads than currently published exitron detection algorithms designed for short-read data. Availability ScanExitronLR is freely available at https://github.com/ylab-hi/ScanExitronLR and distributed as a pip package on the Python Package Index. Supplementary information Supplementary data are available at Bioinformatics online.

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