Relative Calabi-Yau structures and ice quivers with potential

In 2015, Van den Bergh showed that complete 3-Calabi-Yau algebras over an algebraically closed field of characteristic 0 are equivalent to Ginzburg dg algebras associated with quivers with potential. He also proved the natural generalisation to higher dimensions and non-algebraically closed ground fields. The relative version of the notion of Ginzburg dg algebra is that of Ginzburg morphism. For example, every ice quiver with potential gives rise to a Ginzburg morphism. We generalise Van den Bergh's theorem by showing that, under suitable assumptions, any morphism with a relative Calabi-Yau structure is equivalent to a Ginzburg(-Lazaroiu) morphism. In particular, in dimension 3 and over an algebraically closed ground field of characteristic 0, it is given by an ice quiver with potential. Thanks to the work of Bozec-Calaque-Scherotzke, this result can also be viewed as a non-commutative analogue of Joyce-Safronov's Lagrangian neighbourhood theorem in derived symplectic geometry.Comment: 39 pages; v2: more accurate historical account in introduction, reference to Joyce-Safronov's work added, many minor change

AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT

Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesions with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel step-by-step 3D segmentation method to address this problem. We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume of 0.53 on preliminary test set.The code of our work is available on the following link: https://github.com/rightl/autopet.Comment: arXiv admin note: substantial text overlap with arXiv:2209.0121

Yeast synthetic biology advances biofuel production

Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production

Synthetic biology advanced natural product discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions

Identification of Conserved and Novel MicroRNAs in Blueberry

MicroRNAs (miRNAs) are a class of small endogenous RNAs that play important regulatory roles in cells by negatively affecting gene expression at both transcriptional and post-transcriptional levels. There have been extensive studies aiming to identify miRNAs and to elucidate their functions in various plant species. In the present study, we employed the high-throughput sequencing technology to profile miRNAs in blueberry fruits. A total of 9,992,446 small RNA tags with sizes ranged from 18 to 30 nt were obtained, indicating that blueberry fruits have a large and diverse small RNA population. Bioinformatic analysis identified 412 conserved miRNAs belonging to 29 families, and 35 predicted novel miRNAs that are likely to be unique to blueberries. Among them, expression profiles of five conserved miRNAs were validated by stem loop qRT-PCR. Furthermore, the potential target genes of conserved and novel miRNAs were predicted and subjected to Gene Ontology (GO) annotation. Enrichment analysis of the GO-represented biological processes and molecular functions revealed that these target genes were potentially involved in a wide range of metabolic pathways and developmental processes. Particularly, anthocyanin biosynthesis has been predicted to be directly or indirectly regulated by diverse miRNA families. This study is the first report on genome-wide miRNA profile analysis in blueberry and it provides a useful resource for further elucidation of the functional roles of miRNAs during fruit development and ripening