Hop2 Interacts with the Transcription Factor CEBPα and Suppresses Adipocyte Differentiation

CCAAT enhancer binding protein (CEBP) transcription factors (TFs) are known to promote adipocyte differentiation; however, suppressors of CEBP TFs have not been reported thus far. Here, we find that homologous chromosome pairing protein 2 (Hop2) functions as an inhibitor for the TF CEBPα. We found that Hop2 mRNA is highly and specifically expressed in adipose tissue, and that ectopic Hop2 expression suppresses reporter activity induced by CEBP as revealed by DNA transfection. Recombinant and ectopically expressed Hop2 was shown to interact with CEBPα in pull-down and coimmunoprecipitation assays, and interaction between endogenous Hop2 and CEBPα was observed in the nuclei of 3T3 preadipocytes and adipocytes by immunofluorescence and coimmunoprecipitation of nuclear extracts. In addition, Hop2 stable overexpression in 3T3 preadipocytes inhibited adipocyte differentiation and adipocyte marker gene expression. These in vitro data suggest that Hop2 inhibits adipogenesis by suppressing CEBP-mediated transactivation. Consistent with a negative role for Hop2 in adipogenesis, ablation of Hop2 (Hop2−/−) in mice led to increased body weight, adipose volume, adipocyte size, and adipogenic marker gene expression. Adipogenic differentiation of isolated adipose-derived mesenchymal stem cells showed a greater number of lipid droplet–containing colonies formed in Hop2−/− adipose-derived mesenchymal stem cell cultures than in wt controls, which is associated with the increased expression of adipogenic marker genes. Finally, chromatin immunoprecipitation revealed a higher binding activity of endogenous CEBPα to peroxisome proliferator–activated receptor γ, a master adipogenic TF, and a known CEBPα target gene. Therefore, our study identifies for the first time that Hop2 is an intrinsic suppressor of CEBPα and thus adipogenesis in adipocytes

A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers

With the wide application of electric energy storage component arrays, such as battery arrays, capacitor arrays, inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot meet the needs of non-contact and real-time diagnosis for faulty components inside these massive arrays. To solve this problem, this paper proposes a new method based on the beamforming spatial filtering algorithm to precisely locate the faulty components within the arrays in real-time. The method uses highly sensitive magnetometers to collect the magnetic signals from energy storage component arrays, without damaging or even contacting any component. The experimental results demonstrate the potential of the proposed method in securing energy storage component arrays. Within an imaging area of 80 mm $\times$ 80 mm, the one faulty component out of nine total components can be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for battery arrays

Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation

The Natural Language Interface to Databases (NLIDB) empowers non-technical users with database access through intuitive natural language (NL) interactions. Advanced approaches, utilizing neural sequence-to-sequence models or large-scale language models, typically employ auto-regressive decoding to generate unique SQL queries sequentially. While these translation models have greatly improved the overall translation accuracy, surpassing 70% on NLIDB benchmarks, the use of auto-regressive decoding to generate single SQL queries may result in sub-optimal outputs, potentially leading to erroneous translations. In this paper, we propose Metasql, a unified generate-then-rank framework that can be flexibly incorporated with existing NLIDBs to consistently improve their translation accuracy. Metasql introduces query metadata to control the generation of better SQL query candidates and uses learning-to-rank algorithms to retrieve globally optimized queries. Specifically, Metasql first breaks down the meaning of the given NL query into a set of possible query metadata, representing the basic concepts of the semantics. These metadata are then used as language constraints to steer the underlying translation model toward generating a set of candidate SQL queries. Finally, Metasql ranks the candidates to identify the best matching one for the given NL query. Extensive experiments are performed to study Metasql on two public NLIDB benchmarks. The results show that the performance of the translation models can be effectively improved using Metasql

The impact of ESG performance on firms’ technological innovation: evidence from China

Technological innovation is crucial for creating sustainable corporate value and shaping competitive advantage in the market. ESG, as an indicator of corporate value practices, plays a significant role in enterprise technological innovation. However, there is little empirical evidence to support this claim. This study analyzes the relationship between ESG performance and technological innovation in Chinese A-share listed enterprises from 2011 to 2021. The statistical data shows that strong ESG performance has a significant positive impact on corporate technological innovation. ESG performance can promote corporate technological innovation through external mechanisms, such as enhancing corporate network location and increasing institutional shareholding. Additionally, internal mechanisms, such as reducing labor costs and easing financing constraints, can also promote corporate technological innovation. The impact of ESG performance on corporations exhibits heterogeneity, with ESG performance promoting innovation more strongly among labor-intensive firms, non-state-owned firms, highly competitive industries, and mature firms. Based on the study results, it is recommended that enterprises actively practice ESG development concepts, optimize their equity structure, strengthen information communication with stakeholders, and alleviate problems such as information asymmetry to improve their technological innovation. The government should focus on enterprise characteristics, improve ESG development policies, and promote enterprise innovation through ESG performance

Aggregation‐induced emission luminogens for gas sensors

Luminescent chromophores armed with aggregation-induced emission (AIE) characteristics can switch their fluorescence sensing by manipulating the aggregation and disaggregation states, leading to high sensitivity and high signal-to-noise ratio sensors. Accordingly, aggregation-induced emission luminogens (AIEgens) have been widely applied to various biosensing, one of which is the gas sensors. Due to the weak signal, easy diffusion, difficult capture, and instability of gas molecules, electrochemical or infrared tests are generally used for detection. However, electrochemical tests have high power consumption, and the environment easily disturbs infrared tests. Fortunately, photochemical sensors utilizing AIE properties can effectively overcome these deficiencies. AIEgens usually exhibit large Stokes shift, good photostability, and low random blinking, suggesting excellent sensing reproducibility and many achievements have been obtained in AIEgens-based gas sensors. This review summarizes the gas detection mechanism of AIEgens, and enumerate the reported gas sensors based on AIEgens. Then a perspective on the field and challenges facing it are elaborated so that researchers can better understand the development status of this field and develop more AIE-type spectroscopic probes with gas-responsive functions. It is expected to greatly enrich the types of gas sensors and promote the development of the application of AIE properties

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions

PURPLE: Making a Large Language Model a Better SQL Writer

Large Language Model (LLM) techniques play an increasingly important role in Natural Language to SQL (NL2SQL) translation. LLMs trained by extensive corpora have strong natural language understanding and basic SQL generation abilities without additional tuning specific to NL2SQL tasks. Existing LLMs-based NL2SQL approaches try to improve the translation by enhancing the LLMs with an emphasis on user intention understanding. However, LLMs sometimes fail to generate appropriate SQL due to their lack of knowledge in organizing complex logical operator composition. A promising method is to input the LLMs with demonstrations, which include known NL2SQL translations from various databases. LLMs can learn to organize operator compositions from the input demonstrations for the given task. In this paper, we propose PURPLE (Pre-trained models Utilized to Retrieve Prompts for Logical Enhancement), which improves accuracy by retrieving demonstrations containing the requisite logical operator composition for the NL2SQL task on hand, thereby guiding LLMs to produce better SQL translation. PURPLE achieves a new state-of-the-art performance of 80.5% exact-set match accuracy and 87.8% execution match accuracy on the validation set of the popular NL2SQL benchmark Spider. PURPLE maintains high accuracy across diverse benchmarks, budgetary constraints, and various LLMs, showing robustness and cost-effectiveness.Comment: 12 pages, accepted by ICDE 2024 (40th IEEE International Conference on Data Engineering

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

BackgroundIn sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.MethodsA total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.ResultsIn sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).ConclusionThe nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM