Using content-level structures for summarizing microblog repost trees

A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on mi-croblogging sites, we propose a novel re-post tree summarization framework by ef-fectively differentiating two kinds of mes-sages on repost trees called leaders and followers, which are derived from content-level structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summariza-tion model to rank and select salient mes-sages based on the result of leader detec-tion. To reduce the error propagation cas-caded from leader detection, we improve the framework by enhancing the random walk with adjustment steps for sampling from leader probabilities given all the re-posting messages. For evaluation, we construct two annotated corpora, one for leader detection, and the other for repost tree summarization. Experimental results confirm the effectiveness of our method.

Engineering On‐Demand Magnetic Core‐Shell Composite Wound Dressing Matrices via Electrohydrodynamic Micro Scale Printing

Herein, electrohydrodynamic (EHD) printing is utilized to produce well-ordered, dual-drug loaded-magnetic core–shell matrices with high resolution. Coaxial EHD printing is used to load anesthetic lidocaine hydrochloride (LH) and antibiotic tetracycline hydrochloride (TH) in polycaprolactone (PCL) shell formulation and poly (ethylene oxide) (PEO) core formulation, respectively. It is found that when the concentration of PEO is 5% w/w, the fibers exhibit optimum morphology, which is applied in the fabrication of two drug-loaded core–shell fibers. In addition, adding iron oxide (Fe3O4) nanoparticles (NPs) and varying the concentration of TH within the PCL shell layer influence mechanical properties, release behaviors, and cell behaviors of coaxial EHD printing matrices. The addition of Fe3O4 NPs and increasing TH amount in the fibers enhance the mechanical properties of the matrices. Results show rapid release of LH located in the PEO core fibers, while TH loaded in the shell PCL fibers is released sustainably from the coaxial printing matrices. In addition, the sustainable release period for PCL shell layer can be adjusted using Fe3O4 NPs under auxiliary magnetic field. The coaxial drug-loaded matrices also have good bioactivity, indicating the potential of the printed fibers in wound dressings

Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning

In this paper, we focus on automatic disease diagnosis with reinforcement learning (RL) methods in task-oriented dialogues setting. Different from conventional RL tasks, the action space for disease diagnosis (i.e., symptoms) is inevitably large, especially when the number of diseases increases. However, existing approaches to this problem employ a flat RL policy, which typically works well in simple tasks but has significant challenges in complex scenarios like disease diagnosis. Towards this end, we propose to integrate a hierarchical policy of two levels into the dialogue policy learning. The high level policy consists of a model named master that is responsible for triggering a model in low level, the low level policy consists of several symptom checkers and a disease classifier. Experimental results on both self-constructed real-world and synthetic datasets demonstrate that our hierarchical framework achieves higher accuracy in disease diagnosis compared with existing systems. Besides, the datasets (http://www.sdspeople.fudan.edu.cn/zywei/data/Fudan-Medical-Dialogue2.0) and codes (https://github.com/nnbay/MeicalChatbot-HRL) are all available now

Efficacy and Safety of Clearing Heat and Detoxifying Injection in the Treatment of Influenza: A Randomized, Double-Blinded, Placebo-Controlled Trial

Objective. To evaluate the efficacy and safety of CHDI in the treatment of influenza infection. Method. A randomized double-blind, double dummy trial was conducted. Influenza patients with a positive influenza A rapid test diagnosis were randomized to receive CHDI or oseltamivir. Primary outcome was assessed by the median fever alleviation time and clearance time, and secondary outcome was total scores of influenza symptoms. Results. One hundred thirty-nine participants were screened and 34 had a RT-PCR laboratory confirmation of influenza virus infection. Fever alleviation time was 2.5 and 5 hours in CHDI and oseltamivir, respectively, and fever clearance time was 32.5 and 49 hours. The HR of fever alleviation and clearance time shows no significant difference between two groups. Total scores of influenza symptoms descended significantly in both groups after treatment and descended more in CHDI than oseltamivir on day 2. Similar to total symptoms severity score, fever severity score descend more significantly in CHDI than oseltamivir on day 2, and there were no differences on other symptoms. Conclusions. CHDI have a similar effect to oseltamivir in reducing the duration of influenza illness. CHDI was well tolerated, with no serious adverse events noted during the study period

Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models

Large language models (LLMs) have achieved remarkable progress in various natural language processing tasks with emergent abilities. However, they face inherent limitations, such as an inability to access up-to-date information, utilize external tools, or perform precise mathematical reasoning. In this paper, we introduce Chameleon, a plug-and-play compositional reasoning framework that augments LLMs to help address these challenges. Chameleon synthesizes programs to compose various tools, including LLM models, off-the-shelf vision models, web search engines, Python functions, and rule-based modules tailored to user interests. Built on top of an LLM as a natural language planner, Chameleon infers the appropriate sequence of tools to compose and execute in order to generate a final response. We showcase the adaptability and effectiveness of Chameleon on two tasks: ScienceQA and TabMWP. Notably, Chameleon with GPT-4 achieves an 86.54% accuracy on ScienceQA, significantly improving upon the best published few-shot model by 11.37%; using GPT-4 as the underlying LLM, Chameleon achieves a 17.8% increase over the state-of-the-art model, leading to a 98.78% overall accuracy on TabMWP. Further studies suggest that using GPT-4 as a planner exhibits more consistent and rational tool selection and is able to infer potential constraints given the instructions, compared to other LLMs like ChatGPT.Comment: 25 pages, 10 figures. Project page: https://chameleon-llm.github.i

RCN1 induces sorafenib resistance and malignancy in hepatocellular carcinoma by activating c-MYC signaling via the IRE1α–XBP1s pathway

The increasing incidence of hepatocellular carcinoma (HCC) is of great concern globally, but the molecular pathogenesis of these tumors remains unclear. Sorafenib is a first-line drug for the treatment of advanced HCC. However, the efficacy of sorafenib in improving patient survival is limited, and most patients inevitably develop resistance to this drug. Recent studies have demonstrated that the activation of the IRE1α–XBP1s pathway might play a protective role in the response to sorafenib and contribute to malignancy in HCC. Here, we found that RCN1, an endoplasmic reticulum resident protein, is significantly upregulated in sorafenib-resistant HCC cells and promotes tumor progression. Our analysis showed that RCN1 may be an independent predictor of tumor recurrence and overall survival. Mechanistically, RCN1 promotes the dissociation of GRP78 from IRE1α in sorafenib-resistant cells by interacting with GRP78 through its EFh1/2 domain. Subsequently, the IRE1α–XBP1s pathway, a branch of the unfolded protein response, is sustainably activated. Interestingly, IRE1α–XBP1s pathway activity is required for c-MYC signaling, one of the most highly activated oncogenic pathways in HCC. These results suggest that RCN1-targeted therapy might be a feasible strategy for the treatment of HCC