Matrine inhibits hepatocellular carcinoma cell malignancy through the circ_0013290/miR-139-5p/MMP16 pathway

Background. Previous studies have shown the anticancer effect of Matrine on hepatocellular carcinoma (HCC); however, the underlying mechanism is still indistinct. Methods. The expression of circular RNA_0013290 (circ_0013290), microRNA-139-5p (miR-139-5p), matrix metallopeptidase 16 (MMP16), CyclinD1 and N-cadherin was analyzed by quantitative real-time polymerase chain reaction, Western blotting or immuno-histochemistry assay. Cell viability, proliferation, apoptosis, invasion and tube formation were analyzed by cell counting kit-8, 5-Ethynyl-2’-deoxyuridine, flow cytometry analysis, transwell invasion and tube formation assays, respectively. The associations among circ_0013290, miR-139-5p and MMP16 were predicted by starbase online database, and identified by dual-luciferase reporter and RNA pull-down assays. A xenograft mouse model assay was conducted to disclose the effects of circ_0013290 and Matrine on tumor tumorigenesis in vivo. Results. Circ_0013290 and MMP16 expression were significantly upregulated, while miR-139-5p was downregulated in HCC tissues and cells compared with the matched normal liver tissues and cells. Matrine treatment inhibited HCC cell proliferation, invasion and tube formation but induced cell apoptosis, accompanied by the decrease of CyclinD1 and N-cadherin expression; however, these effects were counteracted when circ_0013290 expression was increased. MiR-139-5p depletion or MMP16 introduction relieved Matrine-induced effects in HCC cells. The regulation of circ_0013290 toward HCC cell processes involved MMP16. With respect to the mechanism, circ_0013290 acted as a miR-139-5p sponge, and miR-139-5p targeted MMP16 in HCC cells. Besides, circ_0013290 regulated MMP16 expression through miR-139-5p. Further, circ_0013290 depletion enhanced the inhibitory effects of Matrine on tumor tumorigenesis. Conclusion. Matrine inhibited HCC cell malignancy through the circ_0013290/miR-139-5p/MMP16 pathway, suggesting that Matrine is a potential therapeutic agent for HC

Does Environment Filtering or Seed Limitation Determine Post-Fire Forest Recovery Patterns in Boreal Larch Forests?

Wildfire is a primary natural disturbance in boreal forests, and post-fire vegetation recovery rate influences carbon, water, and energy exchange between the land and atmosphere in the region. Seed availability and environmental filtering are two important determinants in regulating post-fire vegetation recovery in boreal forests. Quantifying how these determinants change over time is helpful for understanding post-fire forest successional trajectory. Time series of remote sensing data offer considerable potential in monitoring the trajectory of post-fire vegetation recovery dynamics beyond current field surveys about structural attributes, which generally lack a temporal perspective across large burned areas. We used a time series of the normalized difference vegetation index (NDVI) and normalized difference shortwave infrared reflectance index (NDSWIR) derived from Landsat images to investigate post-fire dynamics in a Chinese boreal larch forest. An adjacent, unburned patch of a similar forest type and environmental conditions was selected as a control to separate interannual fluctuation in NDVI and NDSWIR caused by climate from changes due to wildfire. Temporal anomalies in NDVI and NDSWIR showed that more than 10 years were needed for ecosystems to recover to a pre-fire state. The boosted regression tree analysis showed that fire severity exerted a persistent, dominant influence on vegetation recovery during the early post-fire successional stage and explained more than 60% of variation in vegetation recovery, whereas distance to the nearest unburned area and environmental conditions exhibited a relatively small influence. This result indicated that the legacy effects of fire disturbance, which control seed availability for tree recruitment, would persist for decades. The influence of environmental filtering could increase with succession and could mitigate the initial heterogeneity in recovery caused by wildfire

Cross-cultural validation of the educational needs assessment tool into Chinese for use in severe knee osteoarthritis

© 2018 Zhao et al. Background: Patient education is an integral part of the management of osteoarthritis. The educational needs assessment tool (ENAT) was developed in the UK to help direct needs-based patient education in rheumatic diseases. Aim: The aim of the study was to adapt and validate the ENAT into Chinese, for use in severe knee osteoarthritis (KOA). Methods: This cross-cultural validation study took two phases: 1) adaptation of the ENAT into Chinese (CENAT) and 2) validation of the CENAT. The Construct validity was determined using factor analysis and criterion-related validity by comparing data from CENAT with data from different self-efficacy scales: patient–physician interactions scale (PEPPI-10), self-efficacy for rehabilitation outcome scale (SER), and the self-efficacy for exercise scale (SEE). Results: The sample comprised 196 patients, with mean age 63.6±8.7 years, disease duration was 11.5 years, and 57.1% were female. The CENAT was found to have high internal consistency. The CENAT had weak correlations with the Chinese versions of PEPPI r=0.40, SER r=0.40, and SEE r=0.39. There were no correlations with age r=−0.03 or disease duration r=−0.11. Conclusion: The ENAT translated well into Chinese and has evidence of validity in KOA. Future studies will further inform its usefulness in clinics, community, and online settings

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence

Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-au\textbf{G}mented sto\textbf{R}y generation framework with a f\textbf{O}rest of e\textbf{V}id\textbf{E}nce (GROVE) to enhance stories' complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an ``asking-why'' prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative's complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.Comment: Findings of EMNLP 202

A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL

Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT and GPT-4 have significantly impacted the AI community, including Text-to-SQL tasks. Some evaluations and analyses on LLMs show their potential to generate SQL queries but they point out poorly designed prompts (e.g. simplistic construction or random sampling) limit LLMs' performance and may cause unnecessary or irrelevant outputs. To address these issues, we propose CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5 for precise control over case-relevant and case-irrelevant knowledge in Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for GPT-3.5, which involves (1) adaptively retrieving cases according to the question intention by de-semantizing the input question, and (2) an adaptive fallback mechanism to ensure the informativeness of the prompt, as well as the relevance between cases and the prompt. In the de-semanticization phase, we designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e detector(mining implicit semantics in hyperbolic space), TextAlign(discovering explicit matches), and Positector (part-of-speech detector). SDRE semantically and syntactically generates in-context exemplar annotations for the new case. On the three cross-domain datasets, our framework outperforms the state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%, respectively

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL. However, it faces challenges with strict SQL syntax requirements. Existing work prompts the LLMs with a list of demonstration examples (i.e. question-SQL pairs) to generate SQL, but the fixed prompts can hardly handle the scenario where the semantic gap between the retrieved demonstration and the input question is large. In this paper, we propose a retrieval-augmented prompting method for a LLM-based Text-to-SQL framework, involving sample-aware prompting and a dynamic revision chain. Our approach incorporates sample-aware demonstrations, which include the composition of SQL operators and fine-grained information related to the given question. To retrieve questions sharing similar intents with input questions, we propose two strategies for assisting retrieval. Firstly, we leverage LLMs to simplify the original questions, unifying the syntax and thereby clarifying the users' intentions. To generate executable and accurate SQLs without human intervention, we design a dynamic revision chain which iteratively adapts fine-grained feedback from the previously generated SQL. Experimental results on three Text-to-SQL benchmarks demonstrate the superiority of our method over strong baseline models

Peritumoral administration of DRibbles-pulsed antigen-presenting cells enhances the antitumor efficacy of anti-GITR and anti-PD-1 antibodies via an antigen presenting independent mechanism.

BACKGROUND: TNF receptor family agonists and checkpoint blockade combination therapies lead to minimal tumor clearance of poorly immunogenic tumors. Therefore, a need to enhance the efficacy of this combination therapy arises. Antigen-presenting cells (APCs) present antigen to T cells and steer the immune response through chemokine and cytokine secretion. DRibbles (DR) are tumor-derived autophagosomes containing tumor antigens and innate inflammatory adjuvants. METHODS: Using preclinical murine lung and pancreatic cancer models, we assessed the triple combination therapy of GITR agonist and PD-1 blocking antibodies with peritumoral injections of DRibbles-pulsed-bone marrow cells (BMCs), which consisted mainly of APCs, or CD103+ cross-presenting dendritic cells (DCs). Immune responses were assessed by flow cytometry. FTY720 was used to prevent T-cell egress from lymph nodes to assess lymph node involvement, and MHC-mismatched-BMCs were used to assess the necessity of antigen presentation by the peritumorally-injected DR-APCs. RESULTS: Tritherapy increased survival and cures in tumor-bearing mice compared to combined antibody therapy or peritumoral DR-BMCs alone. Peritumorally-injected BMCs remained within the tumor for at least 14 days and tritherapy efficacy was dependent on both CD4+ and CD8+ T cells. Although the overall percent of tumor-infiltrating T cells remained similar, tritherapy increased the ratio of effector CD4+ T cells-to-regulatory T cells, CD4+ T-cell cytokine production and proliferation, and CD8+ T-cell cytolytic activity in the tumor. Despite tritherapy-induced T-cell activation and cytolytic activity in lymph nodes, this T-cell activation was not required for tumor regression and enhanced survival. Replacement of DR-BMCs with DR-pulsed-DCs in the tritherapy led to similar antitumor effects, whereas replacement with DRibbles was less effective but delayed tumor growth. Interestingly, peritumoral administration of DR-pulsed MHC-mismatched-APCs in the tritherapy led to similar antitumor effects as MHC-matched-APCs, indicating that the observed enhanced antitumor effect was mediated independently of antigen presentation by the administered APCs. CONCLUSIONS: Overall, these results demonstrate that peritumoral DR-pulsed-BMC/DC administration synergizes with GITR agonist and PD-1 blockade to locally modulate and sustain tumor effector T-cell responses independently of T cell priming and perhaps through innate inflammatory modulations mediated by the DRibbles adjuvant. We offer a unique approach to modify the tumor microenvironment to benefit T-cell-targeted immunotherapies

Locality-Based Visual Outlier Detection Algorithm for Time Series

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value