The transcriptional coactivator TAZ regulates reciprocal differentiation of T(h)17 cells and T(reg) cells

自身免疫性疾病是一类机体对自身抗原发生免疫反应而导致自身多器官、组织受累的慢性炎症性疾病。目前大量研究表明机体内促炎症的TH17细胞和抑制炎症Treg细胞在类群数量和活化状态的失衡是造成自身免疫疾病的主要致病因素。陈兰芬教授和周大旺教授团队的前期研究发现小鼠中Hippo信号通路中激酶Mst1/2缺失导致免疫缺陷，机体易受病原体感染并伴随着严重自身免疫疾病。该研究揭示了Hippo 信号通路转录共激活因子TAZ在决定CD4+初始T细胞分化为促进炎症的TH17效应细胞和抑制免疫反应的Treg调节性细胞过程中发挥着关键作用，拓展了当前对于Hippo信号通路的相关研究内容。 陈兰芬，博士，厦门大学生命科学学院教授。【Abstraact】An imbalance in the lineages of immunosuppressive regulatory T cells (Treg cells) and the inflammatory TH17 subset of helper T cells leads to the development of autoimmune and/or inflammatory disease. Here we found that TAZ, a coactivator of TEAD transcription factors of Hippo signaling, was expressed under T H17 cell–inducing conditions and was required for TH17 differentiation and TH17 cell–mediated inflammatory diseases. TAZ was a critical co-activator of the TH17-defining transcription factor RORγt. In addition, TAZ attenuated Treg cell development by decreasing acetylation of the Treg cell master regulator Foxp3 mediated by the histone acetyltransferase Tip60, which targeted Foxp3 for proteasomal degradation. In contrast, under T regcell–skewing conditions, TEAD1 expression and sequestration of TAZ from the transcription factors RORγt and Foxp3 promoted Treg cell differentiation. Furthermore, deficiency in TAZ or overexpression of TEAD1 induced Treg cell differentiation, whereas expression of a transgene encoding TAZ or activation of TAZ directed TH17 cell differentiation. Our results demonstrate a pivotal role for TAZ in regulating the differentiation of Treg cells and TH17 cells.J. Avruch for comments on the manuscript.Supported by the National Basic Research Program (973) of China (2015CB910502 to L.C.), the National Natural Science Foundation of China (81422018 to L.C.; 31625010 and U1505224 to D.Z.; U1405225 and 81372617 to L.C.; J1310027 to D.Z.; 81472229 to L.H.; and 31600698 to J. Geng), the 111 Projects (B12001 and B06016), China's 1000 Young Talents Program (D.Z., and L.C.), the Fundamental Research Funds for the Central Universities of China-Xiamen University (20720160071 to D.Z. and 20720160054 to L.H.) and Major disease research projects of Xiamen (3502Z20149029 to L.C.)

Unleashing the Expressive Power of Pulse-Based Quantum Neural Networks

Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources. While gate-based QML models are user-friendly for software engineers, their expressivity is restricted by the permissible circuit depth within a finite coherence time. In contrast, pulse-based models enable the construction of "infinitely" deep quantum neural networks within the same time, which may unleash greater expressive power for complex learning tasks. In this paper, this potential is investigated from the perspective of quantum control theory. We first indicate that the nonlinearity of pulse-based models comes from the encoding process that can be viewed as the continuous limit of data-reuploading in gate-based models. Subsequently, we prove that the pulse-based model can approximate arbitrary nonlinear functions when the underlying physical system is ensemble controllable. Under this condition, numerical simulations demonstrate the enhanced expressivity by either increasing the pulse length or the number of qubits. As anticipated, we show through numerical examples that the pulse-based model can unleash more expressive power compared to the gate-based model. These findings lay a theoretical foundation for understanding and designing expressive QML models using NISQ devices.Comment: 12 pages; 6 figure

Prior Bilinear Based Models for Knowledge Graph Completion

Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC). Although bilinear based models have achieved significant advances, these studies mainly concentrate on posterior properties (based on evidence, e.g. symmetry pattern) while neglecting the prior properties. In this paper, we find a prior property named "the law of identity" that cannot be captured by bilinear based models, which hinders them from comprehensively modeling the characteristics of KGs. To address this issue, we introduce a solution called Unit Ball Bilinear Model (UniBi). This model not only achieves theoretical superiority but also offers enhanced interpretability and performance by minimizing ineffective learning through minimal constraints. Experiments demonstrate that UniBi models the prior property and verify its interpretability and performance

Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage

Machine learning models are increasingly utilized across impactful domains to predict individual outcomes. As such, many models provide algorithmic recourse to individuals who receive negative outcomes. However, recourse can be leveraged by adversaries to disclose private information. This work presents the first attempt at mitigating such attacks. We present two novel methods to generate differentially private recourse: Differentially Private Model (DPM) and Laplace Recourse (LR). Using logistic regression classifiers and real world and synthetic datasets, we find that DPM and LR perform well in reducing what an adversary can infer, especially at low FPR. When training dataset size is large enough, we find particular success in preventing privacy leakage while maintaining model and recourse accuracy with our novel LR method.Comment: Proceedings of The Second Workshop on New Frontiers in Adversarial Machine Learning (AdvML-Frontiers @ ICML 2023

Teaching Autonomous Vehicles to Express Interaction Intent during Unprotected Left Turns: A Human-Driving-Prior-Based Trajectory Planning Approach

Incorporating Autonomous Vehicles (AVs) into existing transportation systems necessitates examining their coexistence with Human-driven Vehicles (HVs) in mixed traffic environments. Central to this coexistence is the AVs' ability to emulate human-like interaction intentions within traffic scenarios. We introduce a novel framework for planning unprotected left-turn trajectories for AVs, designed to mirror human driving behaviors and effectively communicate social intentions. This framework consists of three phases: trajectory generation, evaluation, and selection.In the trajectory generation phase, we utilize real human-driving trajectory data to establish constraints for a predicted trajectory space, creating candidate motion trajectories that reflect intent. The evaluation phase incorporates maximum entropy inverse reinforcement learning (ME-IRL) to gauge human trajectory preferences, considering aspects like traffic efficiency, driving comfort, and interactive safety. During the selection phase, a Boltzmann distribution-based approach is employed to assign rewards and probabilities to the candidate trajectories, promoting human-like decision-making. We validate our framework using an authentic trajectory dataset and conduct a comparative analysis with various baseline methods. Our results, derived from simulator tests and human-in-the-loop driving experiments, affirm our framework's superiority in mimicking human-like driving, expressing intent, and computational efficiency. For additional information of this research, please visit https://shorturl.at/jqu35