TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.Comment: Technical Repor

Fertilizer-Holding Performance of Graphene on Soil Colloids Based on Double Electric Layer Theory

Soil nutrient loss, which leads to low plant utilization, has become an urgent issue. Graphene can improve soil fertilizer-holding properties given its small size effect, strong adsorption properties, and large specific surface area. Herein, different amounts of graphene were added to soil samples to study its effect on soil nutrient retention and growth of pepper seedlings. The colloidal double electric layer theory forms the basis for an analysis of variations in soil nutrient concentration through measurements of the zeta potential, which is affected by variations in ion concentrations in soil colloids. We measured the zeta potential of graphene and soil mixed colloids and found that graphene could increase the concentration of nutrient ions in soil colloids. In addition, graphene reduced the loss of nutrients; increased the contents of ammonium nitrogen, effective phosphorus, and fast-acting potassium in the soil after leaching; and enhanced the stability of soil aggregates after leaching. In addition, pepper seedlings grown under graphene treatment for 60 days outperformed seedlings grown without graphene treatment, in terms of plant height and nutrient content. This study demonstrates that the addition of graphene to soil can reduce nutrient loss and promote fertility and plant growth

Risk assessment in systemic lupus erythematosus-associated pulmonary arterial hypertension: CSTAR-PAH cohort study

Objective: This study evaluated the prognostic value of the multivariable risk assessment for systemic lupus erythematosus (SLE)-associated pulmonary arterial hypertension (PAH). Methods: A multicenter prospective cohort of SLE-associated PAH (CSTAR-PAH cohort) diagnosed based on right heart catheterization (RHC) was established. Baseline and follow-up records were collected. Three methods of risk assessment, including (1) the number of low-risk criteria, based on World Health Organization functional class (WHO FC), 6-min walking distance (6MWD), right atrial pressure (RAP), and cardiac index (CI); (2) the three-strata stratification based on the average risk score of four variables (WHO FC, 6MWD, RAP, and CI); and (3) the four-strata stratification based on COMPARE 2.0 model were applied. A risk-assessment method using three noninvasive low-risk criteria was applied at the first follow-up visit. Survival curves between patients with different risk groups were compared by Kaplan–Meier’s estimation and log-rank test. Results: Three-hundred and ten patients were enrolled from 14 PAH centers. All methods of stratification at baseline and first follow-up significantly discriminated long-term survival. Survival rates were also significantly different based on the noninvasive risk assessment in first follow-up visit. Survival deteriorated with the escalation of risk from baseline to first follow-up. Patients with baseline serositis had a higher rate of risk improvement in their follow-up. Conclusion: The risk assessment has a significant prognostic value at both the baseline and first follow-up assessment of SLE-associated PAH. A noninvasive risk assessment can also be useful when RHC is not available during follow-up. Baseline serositis may be a predictor of good treatment response in patients with SLE-associated PAH

A prognostic model for systemic lupus erythematosus-associated pulmonary arterial hypertension: CSTAR-PAH cohort study

Abstract Background Pulmonary arterial hypertension is a major cause of death in systemic lupus erythematosus, but there are no tools specialized for predicting survival in systemic lupus erythematosus-associated pulmonary arterial hypertension. Research question To develop a practical model for predicting long-term prognosis in patients with systemic lupus erythematosus-associated pulmonary arterial hypertension. Methods A prognostic model was developed from a multicenter, longitudinal national cohort of consecutively evaluated patients with systemic lupus erythematosus-associated pulmonary arterial hypertension. The study was conducted between November 2006 and February 2020. All-cause death was defined as the endpoint. Cox regression and least absolute shrinkage and selection operators were used to fit the model. Internal validation of the model was assessed by discrimination and calibration using bootstrapping. Results Of 310 patients included in the study, 81 (26.1%) died within a median follow-up of 5.94 years (interquartile range 4.67–7.46). The final prognostic model included eight variables: modified World Health Organization functional class, 6-min walking distance, pulmonary vascular resistance, estimated glomerular filtration rate, thrombocytopenia, mild interstitial lung disease, N-terminal pro-brain natriuretic peptide/brain natriuretic peptide level, and direct bilirubin level. A 5-year death probability predictive algorithm was established and validated using the C-index (0.77) and a satisfactory calibration curve. Risk stratification was performed based on the predicted probability to improve clinical decision-making. Conclusions This new risk stratification model for systemic lupus erythematosus-associated pulmonary arterial hypertension may provide individualized prognostic probability using readily obtained clinical risk factors. External validation is required to demonstrate the accuracy of this model's predictions in diverse patient populations