TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs. However, real-world complex systems present three prevalent challenges concerning task planning and tool usage: (1) The real system usually has a vast array of APIs, so it is impossible to feed the descriptions of all APIs to the prompt of LLMs as the token length is limited; (2) the real system is designed for handling complex tasks, and the base LLMs can hardly plan a correct sub-task order and API-calling order for such tasks; (3) Similar semantics and functionalities among APIs in real systems create challenges for both LLMs and even humans in distinguishing between them. In response, this paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating within real-world systems. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs for the user task among the extensive array available; (2) LLM Finetuner tunes a base LLM so that the finetuned LLM can be more capable for task planning and API calling; (3) the Demo Selector adaptively retrieves different demonstrations related to hard-to-distinguish APIs, which is further used for in-context learning to boost the final performance. We validate our methods using a real-world commercial system as well as an open-sourced academic dataset, and the outcomes clearly showcase the efficacy of each individual component as well as the integrated framework

Structural responses of large-sized floating wind turbine with consideration of mooring-line dynamics based on coupled FEM simulations

The dynamic response of a 5 MW floating wind turbine is examined using the coupled finite element simulations, and the dynamic effects of the catenary is considered, while the coupling between flexible components are included. The restoring performance of the mooring-line is analysed based on the vector equations and numerical simulations. The stiffness hysteresis and the influence of the catenary dynamics on the restoring performance are studied. Then the structural responses undergoing wind and wave loads, are examined. Our results show that the mooring-line tension significantly rise due to catenary dynamics, and the snap tension gets around three times larger. The mooring-line stiffness presents a hysteresis character, owing to the fluid/structural damping, which becomes more obvious with the increase of motion frequency/amplitude. Moreover, the structural response gets smaller principally because of the hysteresis effect. The spar displacement and the tower root stress become respectively 18.4% and 32.7% smaller

Structural responses of large-sized floating wind turbine with consideration of mooring-line dynamics based on coupled FEM simulations

The dynamic response of a 5 MW floating wind turbine is examined using the coupled finite element simulations, and the dynamic effects of the catenary is considered, while the coupling between flexible components are included. The restoring performance of the mooring-line is analysed based on the vector equations and numerical simulations. The stiffness hysteresis and the influence of the catenary dynamics on the restoring performance are studied. Then the structural responses undergoing wind and wave loads, are examined. Our results show that the mooring-line tension significantly rise due to catenary dynamics, and the snap tension gets around three times larger. The mooring-line stiffness presents a hysteresis character, owing to the fluid/structural damping, which becomes more obvious with the increase of motion frequency/amplitude. Moreover, the structural response gets smaller principally because of the hysteresis effect. The spar displacement and the tower root stress become respectively 18.4% and 32.7% smaller

Fumarate induces LncRNA-MIR4435-2HG to regulate glutamine metabolism remodeling and promote the development of FH-deficient renal cell carcinoma

Abstract Fumarate hydratase (FH) deficient renal cell carcinoma (RCC) is a type of tumor with definite metabolic disorder, but the mechanism of metabolic remodeling is still unclear. LncRNA was reported to closely correlate with cancer metabolism, however the biological role of LncRNA in the development of progression of FH-deficent RCC was not well studied either. FH-deficient RCC samples were collected in my hospital and used for RNA-sequencing and Mass spectrometry analysis. FH-deficient RCC cell line UOK262 and control pFH cells were used for in vitro experiments, including proliferation assay, transwell assay, western-blot, mass spectrometry and so on. PDX mouse model was used for further drug inhibition experiments in vivo. In this study, we analyzed the profiles of LncRNA and mRNA in FH-deficienct RCC samples, and we found that the LncRNA-MIR4435-2GH was specifically highly expressed in FH-deficient RCC compared with ccRCC. In vitro experiments demonstrated that MIR4435-2HG was regulated by Fumarate through histone demethylation, and the deletion of this gene could inhibit glutamine metabolism. RNA-pulldown experiments showed that MIR4435-2HG specifically binds to STAT1, which can transcriptionally activate GLS1. GLS1 inhibitor CB-839 could significantly suppress tumor growth in PDX tumor models. This study analyzed the molecular mechanism of MIR4435-2HG in regulating metabolic remodeling of FH-deficient RCC in clinical samples, cells and animal models by combining transcriptional and metabolic methods. We found that that GLS1 was a therapeutic target for this tumor, and MIR4435-2HG can be used as a drug sensitivity marker

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy

Purpose: Dose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT). Methods and Materials: SPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving ≥20 Gy and mean functional intensity receiving ≥20 Gy (iV20) were calculated. RILT was defined as grade 2+ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dose-function metrics and risk of RILT. Results: By analyzing V/Q normalized intensities and functional distributions across the population, a wide range in functional capability (especially in the ipsilateral lung) was observed in patients with NSCLC before RT. Through multivariable regression models, global lung average dose to the lower lung was found to be significantly associated with RILT, and Q and V iV20 were correlated with RILT when using ipsilateral lung metrics. Through a receiver operating characteristic analysis, combined V/Q low-function receiving ≥20 Gy (low-functioning V/Q20) in the ipsilateral lung was found to be the best predictor (area under the curce: 0.79) of RILT risk. Conclusions: Irradiation of the inferior lung appears to be a locational sensitivity for RILT risk. The multivariable correlation between ipsilateral lung iV20 and RILT, as well as the association of low-functioning V/Q20 and RILT, suggest that irradiating low-functioning regions in the lung may lead to higher toxicity rates

Predicting in-hospital outcomes of patients with acute kidney injury

Abstract Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI