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Atherosclerosis is an important pathological basis for cardiovascular disease. Thus, the treatment of atherosclerosis can effectively improve the prognosis and reduce the mortality of cardiovascular diseases. In this study, we developed simvastatin acid (SA)-loaded cross-linked dendrimer nanoparticles (SA PAM) that were adsorbed to the surface of red blood cells (RBCs) to obtain SA PAM@RBCs, a ROS and shear stress dual response drug delivery system for the treatment of atherosclerosis. SA PAM could continuously release SA in an H2O2-triggered manner, and effectively eliminate excessive H2O2 in LPS-stimulated RAW 264.7 cells, achieving the target of using the special microenvironment at the plaque to release drugs. At the same time, the shear sensitive model also proved that only 12.4% of SA PAM detached from the RBCs under low shear stress (20 dynes per cm2), while 61.3% SA PAM desorbed from the RBCs under a high shear stress (100 dynes per cm2) stimulus, revealing that SA PAM could desorb in response to the shear stress stimulus. Both the FeCl3 model and ApoE-/- model showed that SA PAM@RBCs had better therapeutic effects than free SA, and with excellent safety in vivo. Therefore, a biomimetic drug delivery system with dual sensitivity to ROS and shear stress would become a promising strategy for the treatment of atherosclerosis.

Conversation is an essential component of virtual avatar activities in the metaverse. With the development of natural language processing, textual and vocal conversation generation has achieved a significant breakthrough. Face-to-face conversations account for the vast majority of daily conversations. However, this task has not acquired enough attention. In this paper, we propose a novel task that aims to generate a realistic human avatar face-to-face conversation process and present a new dataset to explore this target. To tackle this novel task, we propose a new framework that utilizes a series of conversation signals, e.g. audio, head pose, and expression, to synthesize face-to-face conversation videos between human avatars, with all the interlocutors modeled within the same network. Our method is evaluated by quantitative and qualitative experiments in different aspects, e.g. image quality, pose sequence trend, and naturalness of the rendering videos. All the code, data, and models will be made publicly available.

We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. Our human study supports that this form of task covers comprehension of multiple types of character persona, including understanding characters' personalities, facts and memories of personal experience, which are well aligned with the psychological and literary theories about the theory of mind (ToM) of human beings on understanding fictional characters during reading. We further propose new model architectures to support the contextualized encoding of long scene texts. Experiments show that our proposed approaches significantly outperform baselines, yet still largely lag behind the (nearly perfect) human performance. Our work serves as a first step toward the goal of narrative character comprehension., Comment: Accepted at NAACL 2022

As has been known since the 90s, there is an integrable structure underlying two-dimensional gravity theories. Recently, two-dimensional gravity theories have regained an enormous amount of attention, but now in relation with quantum chaos - superficially nothing like integrability. In this paper, we return to the roots and exploit the integrable structure underlying dilaton gravity theories to study a late time, large $e^{S_\text{BH}}$ double scaled limit of the spectral form factor. In this limit, a novel cancellation due to the integrable structure ensures that at each genus $g$ the spectral form factor grows like $T^{2g+1}$, and that the sum over genera converges, realising a perturbative approach to the late-time plateau. Along the way, we clarify various aspects of this integrable structure. In particular, we explain the central role played by ribbon graphs, we discuss intersection theory, and we explain what the relations with dilaton gravity and matrix models are from a more modern holographic perspective., Comment: 44 pages + appendices

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io, Comment: v3 is the ICLR camera ready version with some typos fixed. Project site with code: https://react-lm.github.io