Transforming Growth Factor-β Signalling in the Regulation of Skeletal Muscle Regeneration, Fibrosis and Function

Muscle wasting diseases are characterized by the loss of muscle mass, function and regenerative capacity, which negatively affects the quality of life. Transforming growth factor-beta (TGF-β) superfamily members, such as TGF-β, myostatin and activin A, play an important role in reducing muscle mass (atrophy), force and regenerative capacity. Targeting TGF-β signalling is a potential approach to treat muscle wasting. In vivo studies of this thesis show that simultaneous knockout of TGF-β type I receptors Acvr1b and Tgfbr1 in a skeletal muscle-specific manner induced muscle hypertrophy, improved muscle regeneration upon acute injury and increased muscle contractile force. Knockout of both receptors in mice induced more differentially expressed genes in fast-type muscle than slow-type muscle, which were related to muscle growth, contraction, cytoskeleton and metabolism. However, myofibre size increment was not proportional to the increase in contractile force in gastrocnemius. Strikingly, the increase in myofibre size was accompanied by an increase in oxidative metabolism in muscles lacking both type I receptors. In vitro studies show that myotubes produced more collagen I protein than myoblasts. Both TGF-β1 and 3 stimulated collagen production in muscle cells. Noteworthy, siRNA-mediated knockdown of both type I receptors in myotubes reduced their diameter and protein synthesis process, which was associated with the increased expression level of Sntb1 which encodes a unit of dystrophin-glycoprotein complex. Taken together, we identified the regulatory role of TGF-β signalling with respect to muscle adaptation which contributes to the development of treatments for muscle wasting

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Graph neural networks (GNNs) have demonstrated significant promise in modelling relational data and have been widely applied in various fields of interest. The key mechanism behind GNNs is the so-called message passing where information is being iteratively aggregated to central nodes from their neighbourhood. Such a scheme has been found to be intrinsically linked to a physical process known as heat diffusion, where the propagation of GNNs naturally corresponds to the evolution of heat density. Analogizing the process of message passing to the heat dynamics allows to fundamentally understand the power and pitfalls of GNNs and consequently informs better model design. Recently, there emerges a plethora of works that proposes GNNs inspired from the continuous dynamics formulation, in an attempt to mitigate the known limitations of GNNs, such as oversmoothing and oversquashing. In this survey, we provide the first systematic and comprehensive review of studies that leverage the continuous perspective of GNNs. To this end, we introduce foundational ingredients for adapting continuous dynamics to GNNs, along with a general framework for the design of graph neural dynamics. We then review and categorize existing works based on their driven mechanisms and underlying dynamics. We also summarize how the limitations of classic GNNs can be addressed under the continuous framework. We conclude by identifying multiple open research directions

Generalized energy and gradient flow via graph framelets

In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow. By viewing the framelet-based models as discretized gradient flows of some energy, we show it can induce both low-frequency and high-frequency-dominated dynamics, via the separate weight matrices for different frequency components. This substantiates its good empirical performance on both homophilic and heterophilic graphs. We then propose a generalized energy via framelet decomposition and show its gradient flow leads to a novel graph neural network, which includes many existing models as special cases. We then explain how the proposed model generally leads to more flexible dynamics, thus potentially enhancing the representation power of graph neural networks

Generalized Laplacian Regularized Framelet GCNs

This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN. The proposed two models, named p-Laplacian undecimated framelet graph convolution (pL-UFG) and generalized p-Laplacian undecimated framelet graph convolution (pL-fUFG) inherit the nature of p-Laplacian with the expressive power of multi-resolution decomposition of graph signals. The empirical study highlights the excellent performance of the pL-UFG and pL-fUFG in different graph learning tasks including node classification and signal denoising

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges

Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level learning tasks. However, several identified problems, including over-smoothing (OSM), limited expressive power, and over-squashing (OSQ), still limit the performance of MPNNs. In particular, OSQ serves as the latest identified problem, where MPNNs gradually lose their learning accuracy when long-range dependencies between graph nodes are required. In this work, we provide an exposition on the OSQ problem by summarizing different formulations of OSQ from current literature, as well as the three different categories of approaches for addressing the OSQ problem. In addition, we also discuss the alignment between OSQ and expressive power and the trade-off between OSQ and OSM. Furthermore, we summarize the empirical methods leveraged from existing works to verify the efficiency of OSQ mitigation approaches, with illustrations of their computational complexities. Lastly, we list some open questions that are of interest for further exploration of the OSQ problem along with potential directions from the best of our knowledge

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond

Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data. Despite their great success, critical computational challenges such as over-smoothing, over-squashing, and limited expressive power continue to impact the performance of GNNs. In this study, inspired from the time-reversal principle commonly utilized in classical and quantum physics, we reverse the time direction of the graph heat equation. The resulted reversing process yields a class of high pass filtering functions that enhance the sharpness of graph node features. Leveraging this concept, we introduce the Multi-Scaled Heat Kernel based GNN (MHKG) by amalgamating diverse filtering functions' effects on node features. To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power. Notably, we illustrate that all aforementioned issues can be characterized and analyzed via the properties of the filtering functions, and uncover a trade-off between over-smoothing and over-squashing: enhancing node feature sharpness will make model suffer more from over-squashing, and vice versa. Furthermore, we manipulate the time again to show how G-MHKG can handle both two issues under mild conditions. Our conclusive experiments highlight the effectiveness of proposed models. It surpasses several GNN baseline models in performance across graph datasets characterized by both homophily and heterophily

Lack of Tgfbr1 and Acvr1b synergistically stimulates myofibre hypertrophy and accelerates muscle regeneration

In skeletal muscle, transforming growth factor-β (TGF-β) family growth factors, TGF-β1 and myostatin, are involved in atrophy and muscle wasting disorders. Simultaneous interference with their signalling pathways may improve muscle function; however, little is known about their individual and combined receptor signalling. Here, we show that inhibition of TGF-β signalling by simultaneous muscle-specific knockout of TGF-β type I receptors Tgfbr1 and Acvr1b in mice, induces substantial hypertrophy, while such effect does not occur by single receptor knockout. Hypertrophy is induced by increased phosphorylation of Akt and p70S6K and reduced E3 ligases expression, while myonuclear number remains unaltered. Combined knockout of both TGF-β type I receptors increases the number of satellite cells, macrophages and improves regeneration post cardiotoxin-induced injury by stimulating myogenic differentiation. Extra cellular matrix gene expression is exclusively elevated in muscle with combined receptor knockout. Tgfbr1 and Acvr1b are synergistically involved in regulation of myofibre size, regeneration, and collagen deposition

Low-mass dark matter search results from full exposure of PandaX-I experiment

We report the results of a weakly-interacting massive particle (WIMP) dark matter search using the full 80.1\;live-day exposure of the first stage of the PandaX experiment (PandaX-I) located in the China Jin-Ping Underground Laboratory. The PandaX-I detector has been optimized for detecting low-mass WIMPs, achieving a photon detection efficiency of 9.6\%. With a fiducial liquid xenon target mass of 54.0\,kg, no significant excess event were found above the expected background. A profile likelihood analysis confirms our earlier finding that the PandaX-I data disfavor all positive low-mass WIMP signals reported in the literature under standard assumptions. A stringent bound on the low mass WIMP is set at WIMP mass below 10\,GeV/c$^2$, demonstrating that liquid xenon detectors can be competitive for low-mass WIMP searches.Comment: v3 as accepted by PRD. Minor update in the text in response to referee comments. Separating Fig. 11(a) and (b) into Fig. 11 and Fig. 12. Legend tweak in Fig. 9(b) and 9(c) as suggested by referee, as well as a missing legend for CRESST-II legend in Fig. 12 (now Fig. 13). Same version as submitted to PR