Post-translational modifications regulate ??2-Adrenoceptor signaling in cardiac myocytes

??2AR (??2 adrenoceptor) is a prototypical G-protein coupled receptor (GPCR) that plays an important role in cardiovascular and pulmonary physiology through activation of the classic Gs-adenylate cyclase-cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) signaling pathway. In the mammalian heart, increasing cAMP-PKA activity leads to phosphorylation of an array of proteins involved in increasing heart contractility and rate. ??2AR also has a cardiac protective role through utilizing multiple mechanisms to reduce receptor signaling. This includes ??2AR desensitization, ??2AR coupling to G??i, and ??2AR degradation, all of which are tightly regulated by post-translational modifications of the C-terminal region of ??2AR. Over past decades, these modifications have been extensively characterized biochemically in fibroblasts, such as phosphorylation by PKA (serines 261, 262, 345 and 346) and G protein coupled receptor kinases (GRKs) (serines 355, 356 and 364), ubiquitination (lysines 348, 372 and 375) and palmitoylation (cysteine 341). However, the physiological role of these modifications on ??2AR signaling regulation in the heart remains unclear. This study provides new insight into the role of three post-translational modifications on ??2AR signaling regulation in cardiac myocytes. We find that palmitoylation, the fatty acid modification of ??2AR at cysteine 341 is not required for receptor targeting to the plasma membrane caveolae. Instead, both palmitoylation and GRK phosphorylation are required to mediate the association of ??2AR with ??-arrestin 2/ phosphodiesterase 4D complexes to regulate cAMP signaling. In addition, we provide a new mechanism explaining ??2AR coupling from Gas to Gai, which is agonist dose dependent and controlled by both PKA and GRK phosphorylation of the receptor. Moreover, we demonstrate that mutation of either PKA or GRK phosphorylation sites on ??2AR leads to rapid receptor degradation than that of wild type ??2AR. Interestingly, our data also suggest that degradation of ??2AR is coordinated by both lysosomes and proteasomes: the extracellular domains are degraded by lysosomes and the intracellular domains are degraded by proteasomes. Together, all three post-translational modifications coordinate to regulate ??2AR signaling in cardiac tissue under physiological conditions

Sex and Parental Genome Effects on Bovine Fetal Development

During fetal development, the process of forming organs and tissues is mediated by tissuespecific patterns of gene expression. Studying qualitative and quantitative changes in the transcriptome and understanding the mechanisms that regulate gene expression and the association with specific phenotypes in bovine fetal development will help us to explore the sex effect and breed effect. To carry out this work, a well-assembled cattle reference genome is essential, but the current cattle reference genome is incomplete and in particular, missing the Y chromosome. In this thesis I describe the first bovine sex chromosome assemblies for Bos taurus indicus and Bos taurus taurus cattle, that include the complete pseudoautosomal regions (PAR), which span 6.84 Mb and comprises 31 genes, and three Y chromosome X-degenerate (X-d) regions. The results show the ruminant PAR boundary is at a similar position to those of the pig and dog, but that the ruminant PAR extends further than those of human and horse. Differences in the PAR boundaries are consistent with evolutionary divergence times. A bovidae-specific expansion of members of the lipocalin gene family in the PAR reported here, may affect immune-modulation and anti-inflammatory responses in ruminants. Comparison of the X-d regions of Y chromosomes across species revealed that five of the X-Y gametologues, which are known to be global regulators of gene activity and candidate sexual dimorphism genes, are conserved. I report the transcriptome sequencing of 120 samples (60 males and 60 females) and analyzed differences in gene expression between male and female tissues derived from all three germ layers of the embryo, including brain, liver and lung, skeletal muscle and placenta. A remarkably small set of XY genes (gametologues) was identified that differentiate males and females across all tissues. Expression levels of paired gametologues in males and females are unbalanced and explain 18% - 96% of the phenotypic variance in organ weights attributed to the sex effect. Considering the significant programming events at the embryo-fetal stage, we propose that early differences in gametologue expression between females and males are fundamental drivers of phenotypic differences between the sexes.The 120 samples used in this study were from 4 genetic groups: pure Angus, pure Brahman and their reciprocal crosses. Differential gene expression between the pure breed individuals and between the reciprocal crosses was explored. There were 110 genes differentially expressed (DEGs) between pure Angus and pure Brahman in all tissues which were related to functions including immune response and stress response. The DEG between the purebred groups and in the reciprocal crosses showed an additive expression pattern, where both paternal and maternal genomes contributed to the gene expression levels. Only 5% of DEGs in each tissue showed a parent of origin driven expression, Angus or Brahman, and showed both maternal and paternal dominant effects. In summary, the newly assembled cattle sex chromosomes helped us to identify the PAR, X-degenerate region and the locations of gametologues which provide a clear reference for sex-specific study. Studies of sex-specific and breed-specific effects on fetal development showed gametologues play a major role in early female-male phenotypic differentiation which also provided solid evidence to support further parent of origin studies.Thesis (Ph.D.) -- University of Adelaide, School of Animal and Veterinary Sciences, 202

How Sparse Can We Prune A Deep Network: A Geometric Viewpoint

Overparameterization constitutes one of the most significant hallmarks of deep neural networks. Though it can offer the advantage of outstanding generalization performance, it meanwhile imposes substantial storage burden, thus necessitating the study of network pruning. A natural and fundamental question is: How sparse can we prune a deep network (with almost no hurt on the performance)? To address this problem, in this work we take a first principles approach, specifically, by merely enforcing the sparsity constraint on the original loss function, we're able to characterize the sharp phase transition point of pruning ratio, which corresponds to the boundary between the feasible and the infeasible, from the perspective of high-dimensional geometry. It turns out that the phase transition point of pruning ratio equals the squared Gaussian width of some convex body resulting from the $l_1$-regularized loss function, normalized by the original dimension of parameters. As a byproduct, we provide a novel network pruning algorithm which is essentially a global one-shot pruning one. Furthermore, we provide efficient countermeasures to address the challenges in computing the involved Gaussian width, including the spectrum estimation of a large-scale Hessian matrix and dealing with the non-definite positiveness of a Hessian matrix. It is demonstrated that the predicted pruning ratio threshold coincides very well with the actual value obtained from the experiments and our proposed pruning algorithm can achieve competitive or even better performance than the existing pruning algorithms. All codes are available at: https://github.com/QiaozheZhang/Global-One-shot-Prunin

SSRESF: Sensitivity-aware Single-particle Radiation Effects Simulation Framework in SoC Platforms based on SVM Algorithm

The ever-expanding scale of integrated circuits has brought about a significant rise in the design risks associated with radiation-resistant integrated circuit chips. Traditional single-particle experimental methods, with their iterative design approach, are increasingly ill-suited for the challenges posed by large-scale integrated circuits. In response, this article introduces a novel sensitivity-aware single-particle radiation effects simulation framework tailored for System-on-Chip platforms. Based on SVM algorithm we have implemented fast finding and classification of sensitive circuit nodes. Additionally, the methodology automates soft error analysis across the entire software stack. The study includes practical experiments focusing on RISC-V architecture, encompassing core components, buses, and memory systems. It culminates in the establishment of databases for Single Event Upsets (SEU) and Single Event Transients (SET), showcasing the practical efficacy of the proposed methodology in addressing radiation-induced challenges at the scale of contemporary integrated circuits. Experimental results have shown up to 12.78X speed-up on the basis of achieving 94.58% accuracy.Comment: Accepted to the 61th ACM/IEEE Design Automation conference (DAC 2024

Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification

Whole slide image (WSI) refers to a type of high-resolution scanned tissue image, which is extensively employed in computer-assisted diagnosis (CAD). The extremely high resolution and limited availability of region-level annotations make it challenging to employ deep learning methods for WSI-based digital diagnosis. Multiple instance learning (MIL) is a powerful tool to address the weak annotation problem, while Transformer has shown great success in the field of visual tasks. The combination of both should provide new insights for deep learning based image diagnosis. However, due to the limitations of single-level MIL and the attention mechanism's constraints on sequence length, directly applying Transformer to WSI-based MIL tasks is not practical. To tackle this issue, we propose a Multi-level MIL with Transformer (MMIL-Transformer) approach. By introducing a hierarchical structure to MIL, this approach enables efficient handling of MIL tasks that involve a large number of instances. To validate its effectiveness, we conducted a set of experiments on WSIs classification task, where MMIL-Transformer demonstrate superior performance compared to existing state-of-the-art methods. Our proposed approach achieves test AUC 94.74% and test accuracy 93.41% on CAMELYON16 dataset, test AUC 99.04% and test accuracy 94.37% on TCGA-NSCLC dataset, respectively. All code and pre-trained models are available at: https://github.com/hustvl/MMIL-Transforme