Contribution of de novo retroelements to birth defects and childhood cancers.

Insertion of active retroelements-L1s, Alus, and SVAs-can disrupt proper genome function and lead to various disorders including cancer. However, the role of de novo retroelements (DNRTs) in birth defects and childhood cancers has not been well characterized due to the lack of adequate data and efficient computational tools. Here, we examine whole-genome sequencing data of 3,244 trios from 12 birth defect and childhood cancer cohorts in the Gabriella Miller Kids First Pediatric Research Program. Using an improved version of our tool xTea (x-Transposable element analyzer) that incorporates a deep-learning module, we identified 162 DNRTs, as well as 2 pseudogene insertions. Several variants are likely to be causal, such as a de novo Alu insertion that led to the ablation of a whole exon in the NF1 gene in a proband with brain tumor. We observe a high de novo SVA insertion burden in both high-intolerance loss-of-function genes and exons as well as more frequent de novo Alu insertions of paternal origin. We also identify potential mosaic DNRTs from embryonic stages. Our study reveals the important roles of DNRTs in causing birth defects and predisposition to childhood cancers.

Biogas production prediction model of food waste anaerobic digestion for energy optimization using mixup data augmentation-based global attention mechanism.

Achieving rapid, efficient, and cost-effective anaerobic digestion (AD) of food waste is a key means to improve the efficiency of food waste treatment. However, in view of the shortage of historical anaerobic digestion data, the limitation of general neural networks in predicting biogas production, and its sensitivity to abnormal variation points, achieving accurate prediction of biogas production is not easy. This paper proposes a novel biogas production prediction model of food waste AD for energy optimization based on the mixup data augmentation integrating an improved global attention mechanism long short-term memory (LSTM). Taking the AD data of the actual factory as samples, the mixup data augmentation is introduced to generate virtual samples with the similar distribution as original samples. Then original samples and generated virtual samples are used as the input of the global attention mechanism LSTM to establish the food waste AD biogas production prediction model. Finally, the proposed method is applied in the biogas production prediction of actual food waste treatment plants. Compared with other industrial modeling models, the experimental results show that the proposed method has the highest prediction accuracy of 0.988, which performs well in predicting biogas production and can effectively guide and timely adjust feed configuration of AD plants.

Near-gapless and haplotype-resolved apple genomes provide insights into the genetic basis of rootstock-induced dwarfing.

Dwarfing rootstocks have transformed the production of cultivated apples; however, the genetic basis of rootstock-induced dwarfing remains largely unclear. We have assembled chromosome-level, near-gapless and haplotype-resolved genomes for the popular dwarfing rootstock 'M9', the semi-vigorous rootstock 'MM106' and 'Fuji', one of the most commonly grown apple cultivars. The apple orthologue of auxin response factor 3 (MdARF3) is in the Dw1 region of 'M9', the major locus for rootstock-induced dwarfing. Comparing 'M9' and 'MM106' genomes revealed a 9,723-bp allele-specific long terminal repeat retrotransposon/gypsy insertion, DwTE, located upstream of MdARF3. DwTE is cosegregated with the dwarfing trait in two segregating populations, suggesting its prospective utility in future dwarfing rootstock breeding. In addition, our pipeline discovered mobile mRNAs that may contribute to the development of dwarfed scion architecture. Our research provides valuable genomic resources and applicable methodology, which have the potential to accelerate breeding dwarfing rootstocks for apple and other perennial woody fruit trees.