2,085 research outputs found

    The landscape, properties, and determinants of transcriptional activation of endogenous transposable elements in grapevine (Vitis vinifera L.) : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

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    Transposable elements (TEs) are an intrinsic mutagen of eukaryotic genomes and have been proposed to be important in increasing genetic diversity in plants. It has been known that biotic and abiotic stress treatments induce TE transcription, the first stage in TE mobilisation. This research began with an investigation of TE transcription activity in grapevine embryogenic callus subjected to biotic stressors (Botrytis cinerea extracts and live Hanseniaspora uvarum cultures) to determine the location and regulation of autonomous TEs. Short-read RNA sequencing (RNAseq) has been commonly used to determine TE transcription patterns at a family level. This research sought to further these approaches by establishing an analysis pipeline to identify the expression of individual TE loci from Illumina RNAseq data. We efficiently identified that only 1.7%-2.5% of total annotated TE loci were transcribed in our system. This work identified a strong tendency for TE expression candidates to be found within introns of expressed genes. It was also discovered that these pairs of TEs and genes shared the same differential expression patterns in response to applied stressors. Our analysis pipeline was successfully validated using publically available RNAseq datasets from Arabidopsis, wild-type and epigenetic mutant (ibm2 and ddm1) lines, and Drosophila datasets of amyotrophic lateral sclerosis (ALS) models exhibiting a TE transcriptional storm. We successfully identified an Arabidopsis COPIA-93 locus previously proven to mobilise in ddm1 mutant and a subset of Drosophila TE loci that potentially contributed to full-length autonomous TE transcripts in the ALS models that have not been previously reported. Oxford Nanopore Technology (ONT) cDNA sequencing was deployed to determine whether autonomous TEs were being expressed as a precursor of mobilisation. Only low levels of full-length transcription of one Gypsy-V1 locus and three hAT-7 loci was detected in this data, suggesting rare intact transcription from autonomous TE loci despite stress treatments. This finding suggested that TE mobilisation might require inhibition of the epigenetic silencing system. We, therefore, treated embryogenic callus with the histone deacetylase inhibitors (HDACi), trichostatin A (TSA) or 4-phenylbutyric acid (4PBA), to alter the heterochromatic architecture of callus cells. Only the 4PBA treatment showed a noticeable shift in the transcriptional landscape of TE transcription, significantly increasing the proportion of intergenic TE loci in the expression candidate pool and resulting in significant up-regulation of 2,059 TE loci. ONT cDNA sequencing of these samples detected very low levels of intact sequencing reads from different yet a single Gypsy-V1 locus and six hAT-7 loci. Five genes participating in the RNA-dependent DNA methylation (RdDM) pathway (AGO2, AGO4, RDR1, RDR6, and NERD) were upregulated, suggesting that callus exposed to 4PBA responded by an enhancement of RdDM, maintaining effective control of TE transcription and therefore TE mobility. Overall, this thesis contributes to the understanding of the landscape, properties, and determinants of transcriptional activation of endogenous transposable elements, revealing the closely connected transcriptional relationship between TEs and co-localised genes. These findings shed light on the genetic and epigenetic impact of endogenous TE activation on genes in nature

    Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic Scenes

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    In this paper, we study multi-label atomic activity recognition. Despite the notable progress in action recognition, it is still challenging to recognize atomic activities due to a deficiency in a holistic understanding of both multiple road users' motions and their contextual information. In this paper, we introduce Action-slot, a slot attention-based approach that learns visual action-centric representations, capturing both motion and contextual information. Our key idea is to design action slots that are capable of paying attention to regions where atomic activities occur, without the need for explicit perception guidance. To further enhance slot attention, we introduce a background slot that competes with action slots, aiding the training process in avoiding unnecessary focus on background regions devoid of activities. Yet, the imbalanced class distribution in the existing dataset hampers the assessment of rare activities. To address the limitation, we collect a synthetic dataset called TACO, which is four times larger than OATS and features a balanced distribution of atomic activities. To validate the effectiveness of our method, we conduct comprehensive experiments and ablation studies against various action recognition baselines. We also show that the performance of multi-label atomic activity recognition on real-world datasets can be improved by pretraining representations on TACO. We will release our source code and dataset. See the videos of visualization on the project page: https://hcis-lab.github.io/Action-slot

    3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling

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    For monocular depth estimation, acquiring ground truths for real data is not easy, and thus domain adaptation methods are commonly adopted using the supervised synthetic data. However, this may still incur a large domain gap due to the lack of supervision from the real data. In this paper, we develop a domain adaptation framework via generating reliable pseudo ground truths of depth from real data to provide direct supervisions. Specifically, we propose two mechanisms for pseudo-labeling: 1) 2D-based pseudo-labels via measuring the consistency of depth predictions when images are with the same content but different styles; 2) 3D-aware pseudo-labels via a point cloud completion network that learns to complete the depth values in the 3D space, thus providing more structural information in a scene to refine and generate more reliable pseudo-labels. In experiments, we show that our pseudo-labeling methods improve depth estimation in various settings, including the usage of stereo pairs during training. Furthermore, the proposed method performs favorably against several state-of-the-art unsupervised domain adaptation approaches in real-world datasets.Comment: Accepted in ECCV 2022. Project page: https://ccc870206.github.io/3D-PL

    VividDream: Generating 3D Scene with Ambient Dynamics

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    We introduce VividDream, a method for generating explorable 4D scenes with ambient dynamics from a single input image or text prompt. VividDream first expands an input image into a static 3D point cloud through iterative inpainting and geometry merging. An ensemble of animated videos is then generated using video diffusion models with quality refinement techniques and conditioned on renderings of the static 3D scene from the sampled camera trajectories. We then optimize a canonical 4D scene representation using an animated video ensemble, with per-video motion embeddings and visibility masks to mitigate inconsistencies. The resulting 4D scene enables free-view exploration of a 3D scene with plausible ambient scene dynamics. Experiments demonstrate that VividDream can provide human viewers with compelling 4D experiences generated based on diverse real images and text prompts.Comment: Project page: https://vivid-dream-4d.github.i
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