Functional impact of microRNA-34a on stem cell differentiation towards smooth muscle cell

PhDMicroRNAs play an important role in biological regulation. Recently miR-34a has been reported to regulate tumour cell cycle progression and apoptosis. However, the functional role of miR-34a in smooth muscle cell (SMC) differentiation from stem cells is yet unclear. Main objectives of this PhD project are to determine the functional role of miR-34a and its target genes in SMC differentiation and underlying mechanisms. Mouse embryonic stem (ES) cells were seeded on collagen coated flasks in differentiation medium to allow SMC differentiation. Upon analysis, miR-34a was significantly up-regulated during SMC differentiation. Results demonstrated that overexpression of miR-34a significantly promoted SMC-specific gene expression, while knockdown of miR-34a inhibited expression of SMC specific gene. Enforced expression and knockdown of miR-34a in differentiating ES cells up-regulated and down-regulated, respectively, several SMC transcription factors in a similar manner. It was also found that miR-34a overexpression in stem cells promoted SMC differentiation in vivo. Furthermore, deacetylase sirtuin 1 (Sirt1) was identified as one of the top targets of miR-34a. Surprisingly, Sirt1 was demonstrated to be positively regulated by miR-34a during SMC differentiation in a cellular context and RNA sequence dependent manner. VIII Mechanistically, the data suggested that miR-34a promoted differentiating stem cells arrest at G0/G1 phase, and a significant decreased incorporation of miR-34a and SirT1 RNA into Ago2-RISC complex was observed upon SMC differentiation. The results demonstrated that Sirt1 acted as a transcriptional activator in the regulation of SMC gene during ES cell differentiation. Finally, H3K9 tri-methylation around the promoter regions of the SMαA and SM22α genes was also found to be significantly inhibited by SirT1 overexpression. These findings suggest that miR-34a plays an important role in SMC differentiation from ES cells. Meanwhile, Sirt1 can be regulated by miR-34a through an unexpected pathway and it was identified as a functional modulating target in miR-34a mediated SMC differentiation.British Heart Foundation (FS/09/044/28007, PG/11/40/28891, PG/13/45/30326); Queen Mary University of Londo

Learning to Annotate Part Segmentation with Gradient Matching

The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited.Comment: ICLR 202

SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking

Due to the ambiguity of homophones, Chinese Spell Checking (CSC) has widespread applications. Existing systems typically utilize BERT for text encoding. However, CSC requires the model to account for both phonetic and graphemic information. To adapt BERT to the CSC task, we propose a token-level self-distillation contrastive learning method. We employ BERT to encode both the corrupted and corresponding correct sentence. Then, we use contrastive learning loss to regularize corrupted tokens' hidden states to be closer to counterparts in the correct sentence. On three CSC datasets, we confirmed our method provides a significant improvement above baselines

Beyond Fairness: Age-Harmless Parkinson's Detection via Voice

Parkinson's disease (PD), a neurodegenerative disorder, often manifests as speech and voice dysfunction. While utilizing voice data for PD detection has great potential in clinical applications, the widely used deep learning models currently have fairness issues regarding different ages of onset. These deep models perform well for the elderly group (age $>$ 55) but are less accurate for the young group (age $\leq$ 55). Through our investigation, the discrepancy between the elderly and the young arises due to 1) an imbalanced dataset and 2) the milder symptoms often seen in early-onset patients. However, traditional debiasing methods are impractical as they typically impair the prediction accuracy for the majority group while minimizing the discrepancy. To address this issue, we present a new debiasing method using GradCAM-based feature masking combined with ensemble models, ensuring that neither fairness nor accuracy is compromised. Specifically, the GradCAM-based feature masking selectively obscures age-related features in the input voice data while preserving essential information for PD detection. The ensemble models further improve the prediction accuracy for the minority (young group). Our approach effectively improves detection accuracy for early-onset patients without sacrificing performance for the elderly group. Additionally, we propose a two-step detection strategy for the young group, offering a practical risk assessment for potential early-onset PD patients

Learning to Annotate Part Segmentation with Gradient Matching

The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited

Focal surfaces of discrete geometry

The differential geometry of smooth three-dimensional surfaces can be interpreted from one of two perspectives: in terms of oriented frames located on the surface, or in terms of a pair of associated focal surfaces. These focal surfaces are swept by the loci of the principal curvatures' radii. In this article, we develop a focal-surface-based differential geometry interpretation for discrete mesh surfaces. Focal surfaces have many useful properties. For instance, the normal of each focal surface indicates a principal direction of the corresponding point on the original surface. We provide algorithms to robustly approximate the focal surfaces of a triangle mesh with known or estimated normals. Our approach locally parameterizes the surface normals about a point by their intersections with a pair of parallel planes. We show neighboring normal triplets are constrained to pass simultaneously through two slits, which are parallel to the specified parametrization planes and rule the focal surfaces. We develop both CPU and GPU-based algorithms to efficiently approximate these two slits and, hence, the focal meshes. Our focal mesh estimation also provides a novel discrete shape operator that simultaneously estimates the principal curvatures and principal directions.Engineering and Applied Science