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Penicillium expansum is the most popular post-harvest pathogen and causes blue mold disease in pome fruit and leads to significant economic losses worldwide every year. However, the fundamental regulation mechanisms of growth in P. expansum are unclear. Recently, non-coding RNAs (ncRNAs) have attracted more attention due to critical roles in normalizing gene expression and maintaining cellular genotypes in organisms. However, the research related to ncRNAs in P. expansum have not been reported. Therefore, to provide an overview of ncRNAs on composition, distribution, expression changes, and potential targets in the growth process, a comparative transcriptomic analysis was performed on spores and mycelia of P. expansum in the present study. A total of 2595 novel mRNAs, 3362 long non-coding RNAs (lncRNAs), 10 novel microRNAs (miRNAs), 86 novel small interfering RNAs (siRNAs), and 11,238 circular RNAs (circRNAs) were predicted and quantified. Of these, 1482 novel mRNAs, 5987 known mRNAs, 2047 lncRNAs, 40 miRNAs, 38 novel siRNAs, and 9235 circRNAs were differentially expressed (DE) in response to the different development stages. Afterward, the involved functions and pathways of DE RNAs were revealed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment analysis. The interaction networks between mRNAs, lncRNAs, and miRNAs were also predicted based on their correlation coefficient of expression profiles. Among them, it was found that miR168 family members may play important roles in fungal growth due to their central location in the network. These findings will contribute to a better understanding on regulation machinery at the RNA level on fungal growth and provide a theoretical basis to develop novel control strategies against P. expansum.

Natural language sampling (NLS) is a common methodology in research and clinical practice used to evaluate a child's spontaneous spoken language in a naturalistic context. Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that results in heterogeneous language profiles. NLS has emerged as a useful method for better understanding language use and development in this population. Prior work has examined the effects that contexts (e.g., home, lab) and conversational partners (e.g., examiner, parent) have on childrens language production, but less is known about remote collection of interactions between parents and children with ASD at home. Increasing our understanding of in-home remote NLS with children with ASD will improve naturalistic approaches to language assessment in children with ASD. We analyzed natural language samples of 90 dyads of parents and four- to seven-year old children with ASD collected remotely in the home using items and activities from the family's own home. The 15-min parent-child interactions were transcribed and analyzed for the child's language level measured by the number of different words. We examined the range of activities and the relationship between activities and the child's language level. We found that in-home parent-child activities fell into 13 descriptive categories, but we found no significant difference in child's language level (measured by the mean number of different words) across activities. We found that dyads involving children with higher language levels engaged in significantly fewer different activities compared to children with lower language levels. We found no difference in the number of different words elicited in the five most frequent activities in our sample. These results support the feasibility of remote in-home language sampling. While the types of activities that parent-child dyads engaged in did not affect the richness of language elicited, the number of different activities was associated with the child's language level. Allowing parents to steer children with lower language levels toward more different activities may allow children with lower language to more fully demonstrate their spoken language abilities.