DESIGN, MODELING, AND CONTROL OF SOFT DYNAMIC SYSTEMS

Soft physical systems, be they elastic bodies, fluids, and compliant-bodied creatures, are ubiquitous in nature. Modeling and simulation of these systems with computer algorithms enable the creation of visually appealing animations, automated fabrication paradigms, and novel user interfaces and control mechanics to assist designers and engineers to develop new soft machines. This thesis develops computational methods to address the challenges emerged during the automation of the design, modeling, and control workflow supporting various soft dynamic systems. On the design/control side, we present a sketch-based design interface to enable non-expert users to design soft multicopters. Our system is endorsed by a data-driven algorithm to generate system identification and control policies given a novel shape prototype and rotor configurations. We show that our interactive system can automate the workflow of different soft multicopters\u27 design, simulation, and control with human designers involved in the loop. On the modeling side, we study the physical behaviors of fluidic systems from a local, collective perspective. We develop a prior-embedded graph network to uncover the local constraint relations underpinning a collective dynamic system such as particle fluid. We also proposed a simulation algorithm to model vortex dynamics with locally interacting Lagrangian elements. We demonstrate the efficacy of the two systems by learning, simulating and visualizing complicated dynamics of incompressible fluid

Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules

Masked graph modeling excels in the self-supervised representation learning of molecular graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three key components: (1) graph tokenizer, which breaks a molecular graph into smaller fragments (i.e., subgraphs) and converts them into tokens; (2) graph masking, which corrupts the graph with masks; (3) graph autoencoder, which first applies an encoder on the masked graph to generate the representations, and then employs a decoder on the representations to recover the tokens of the original graph. However, the previous MGM studies focus extensively on graph masking and encoder, while there is limited understanding of tokenizer and decoder. To bridge the gap, we first summarize popular molecule tokenizers at the granularity of node, edge, motif, and Graph Neural Networks (GNNs), and then examine their roles as the MGM's reconstruction targets. Further, we explore the potential of adopting an expressive decoder in MGM. Our results show that a subgraph-level tokenizer and a sufficiently expressive decoder with remask decoding have a large impact on the encoder's representation learning. Finally, we propose a novel MGM method SimSGT, featuring a Simple GNN-based Tokenizer (SGT) and an effective decoding strategy. We empirically validate that our method outperforms the existing molecule self-supervised learning methods. Our codes and checkpoints are available at https://github.com/syr-cn/SimSGT.Comment: NeurIPS 2023. 10 page

Silencing DTX3L Inhibits the Progression of Cervical Carcinoma by Regulating PI3K/AKT/mTOR Signaling Pathway

Cervical carcinoma (CC) is the second most prevalent gynecologic cancer in females across the world. To obtain a better understanding of the mechanisms underlying the development of CC, high-resolution label-free mass spectrometry was performed on CC and adjacent normal tissues from eight patients. A total of 2631 proteins were identified, and 46 significant differently expressed proteins (DEPs) were found between CC and normal tissues (p &lt; 0.01, fold change &gt;10 or &lt;0.1). Ingenuity pathway analysis revealed that the majority of the proteins were involved in the regulation of eIF4 and p70S6K signaling and mTOR signaling. Among 46 DEPs, Integrin beta 6 (ITGB6), PPP1CB, TMPO, PTGES3 (P23) and DTX3L were significantly upregulated, while Desmin (DES) was significantly downregulated in CC tissues compared with the adjacent normal tissues. In in vivo and in vitro experiments, DTX3L knockdown suppressed CC cell proliferation, migration, invasion and xenograft tumorigenesis, and enhanced cell apoptosis. Combination of silencing DTX3L and cisplatin treatment induced higher apoptosis percentage compared to cisplatin treatment alone. Moreover, DTX3L silencing inhibited the PI3K/AKT/mTOR signal pathway. Thus, our results suggested DTX3L could regulate CC progression through the PI3K/AKT/mTOR signal pathway and is potentially a novel biomarker and therapeutic target for CC.De två första författarna delar förstaförfattarskapet.</p

Silencing DTX3L Inhibits the Progression of Cervical Carcinoma by Regulating PI3K/AKT/mTOR Signaling Pathway

Cervical carcinoma (CC) is the second most prevalent gynecologic cancer in females across the world. To obtain a better understanding of the mechanisms underlying the development of CC, high-resolution label-free mass spectrometry was performed on CC and adjacent normal tissues from eight patients. A total of 2631 proteins were identified, and 46 significant differently expressed proteins (DEPs) were found between CC and normal tissues (p 10 or <0.1). Ingenuity pathway analysis revealed that the majority of the proteins were involved in the regulation of eIF4 and p70S6K signaling and mTOR signaling. Among 46 DEPs, Integrinβ6 (ITGB6), PPP1CB, TMPO, PTGES3 (P23) and DTX3L were significantly upregulated, while Desmin (DES) was significantly downregulated in CC tissues compared with the adjacent normal tissues. In in vivo and in vitro experiments, DTX3L knockdown suppressed CC cell proliferation, migration, invasion and xenograft tumorigenesis, and enhanced cell apoptosis. Combination of silencing DTX3L and cisplatin treatment induced higher apoptosis percentage compared to cisplatin treatment alone. Moreover, DTX3L silencing inhibited the PI3K/AKT/mTOR signal pathway. Thus, our results suggested DTX3L could regulate CC progression through the PI3K/AKT/mTOR signal pathway and is potentially a novel biomarker and therapeutic target for CC