Background The regulation of genes related to lipid metabolism by genetic engineering is an important way to increase the accumulation of lipids in microalgae. DNA binding with one finger (DOF) is a plant-specific transcription factor in higher plants, where it regulates carbon and nitrogen metabolic pathways by regulating key genes involved in these pathways. Overexpression of DOF can increase lipid production in plants; however, it is not clear whether overexpression of DOF can increase lipids in microalgae. Results In this study, we cloned a DOF transcription factor, crDOF, from Chlamydomonas reinhardtii . The sequence of this transcription factor is 1875 bp and encodes a peptide of 624 amino acids with a conserved DOF domain. Overexpression of crDOF in C. reinhardtii significantly increased the intracellular lipid content. The content of total fatty acids in the transgenic algae line Tran c-crDOF -12 was 126.01 μg/mg (dry weight), which was 23.24% higher than that of the wild type. Additionally, the content of unsaturated fatty acids in the transgenic Tran c-crDOF -12 line increased significantly. Fluorescence quantitative PCR analysis showed that in the transgenic line Tran c-crDOF -12, the expression levels of BCC1, FAT1, SQD1, MGD1, DGD1 and PGP1 genes were significantly upregulated, while the expression levels of ACP1, ACS1, CIS1 and SQD2 were downregulated. Conclusions Our results confirm that crDOF increases intracellular lipids in C. reinhardtii by regulating key genes involved in lipid metabolism. According to these findings, we propose that enhancing the lipid content in microalgae by overexpressing DOF may be achieved in other industrial strains of microalgae and be employed for the industrial production of biodiesel. Electronic supplementary material The online version of this article (10.1186/s13068-019-1403-1) contains supplementary material, which is available to authorized users.

This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text's toxicity within a text, which is crucial for semiautomated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.

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Multimodal fusion is a core problem for multimodal sentiment analysis. Previous works usually treat all three modal features equally and implicitly explore the interactions between different modalities. In this paper, we break this kind of methods in two ways. Firstly, we observe that textual modality plays the most important role in multimodal sentiment analysis, and this can be seen from the previous works. Secondly, we observe that comparing to the textual modality, the other two kinds of nontextual modalities (visual and acoustic) can provide two kinds of semantics, shared and private semantics. The shared semantics from the other two modalities can obviously enhance the textual semantics and make the sentiment analysis model more robust, and the private semantics can be complementary to the textual semantics and meanwhile provide different views to improve the performance of sentiment analysis together with the shared semantics. Motivated by these two observations, we propose a text-centered shared-private framework (TCSP) for multimodal fusion, which consists of the cross-modal prediction and sentiment regression parts. Experiments on the MOSEI and MOSI datasets demonstrate the effectiveness of our shared-private framework, which outperforms all baselines. Furthermore, our approach provides a new way to utilize the unlabeled data for multimodal sentiment analysis.

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