An increasing number of long noncoding RNAs (lncRNAs) have been discovered with the recent advances in RNA-sequencing technologies. lncRNAs play key roles across diverse biological processes, and are involved in developmental regulation. However, knowledge about how the genome-wide expression of lncRNAs is developmentally regulated is still limited. We here performed a whole-genome identification of lncRNAs followed by a global expression profiling of these lncRNAs during development in Drosophila melanogaster. We combined bioinformatic prediction of lncRNAs with stringent filtering of protein-coding transcripts and experimental validation to define a high-confidence set of Drosophila lncRNAs. We identified 1,077 lncRNAs in the given transcriptomes that contain 43,967 transcripts; among these, 646 lncRNAs are novel. In vivo expression profiling of these lncRNAs in 27 developmental processes revealed that the expression of lncRNAs is highly temporally restricted relative to that of protein-coding genes. Remarkably, 21% and 42% lncRNAs were significantly upregulated at late embryonic and larval stage, the critical time for developmental transition. The results highlight the developmental specificity of lncRNA expression, and reflect the regulatory significance of a large subclass of lncRNAs for the onset of metamorphosis. The systematic annotation and expression analysis of lncRNAs during Drosophila development form the foundation for future functional exploration.

Measuring associations is an important scientific task. A novel measurement method maximal information coefficient (MIC) was proposed to identify a broad class of associations. As foreseen by its authors, MIC implementation algorithm ApproxMaxMI is not always convergent to real MIC values. An algorithm called SG (Simulated annealing and Genetic) was developed to facilitate the optimal calculation of MIC, and the convergence of SG was proved based on Markov theory. When run on fruit fly data set including 1,000,000 pairs of gene expression profiles, the mean squared difference between SG and the exhaustive algorithm is 0.00075499, compared with 0.1834 in the case of ApproxMaxMI. The software SGMIC and its manual are freely available at http://lxy.depart.hebust.edu.cn/SGMIC/SGMIC.htm.

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In April 2020, 232 tombs of the Western Han Dynasty were found in Sundayuan, Heze City. In total, 141 pottery figurines of significant historical, cultural, and artistic value were unearthed from the tombs. Some of the figurines are currently being stored in warehouses, and the surface of some of the figurines show fungal deterioration. To thoroughly analyze the fungal deterioration on the surface of the pottery figurines and find appropriate control methods, we used high-through sequencing, scanning electron microscopy observation, pure cultures of culturable fungi, and optical microscopy observation and molecular identification of culturable fungi. We conducted fungistatic and simulation experiments in the laboratory to find appropriate control methods. We found that the fungi on the surface of the figurines were mainly of the phylum Ascomycota, and a few fungi were of the phyla Basidiomycota and Mucoromycota. We isolated seven culturable fungal strains and observed their colony morphology. The seven fungal strains were Lecanicillium aphanocladii, Penicillium aurantiogriseum, Clonostachys rosea, Mortierella sp., Mortierella alpina, Aspergillus flavus, and Cladosporium halotolerans. Through the fungistatic and simulation experiments conducted in the laboratory, we found that 50 mg/ml cinnamaldehyde and 0.5% K100 (2-methyl-4-isothiazolin-3-one) have a good fungistatic effect. They can not only inhibit the growth of fungi on medium, but also inhibit the growth of fungi on the surface of pottery figurines. This study has good reference significance for the analysis and control of fungal deterioration of unearthed pottery figurines.