STAT3 potentiates RNA polymerase I-directed transcription and tumor growth by activating RPA34 expression

Background: Deregulation of either RNA polymerase I (Pol I)-directed transcription or expression of signal transducer and activator of transcription 3 (STAT3) correlates closely with tumorigenesis. However, the connection between STAT3 and Pol I-directed transcription hasn’t been investigated. Methods: The role of STAT3 in Pol I-directed transcription was determined using combined techniques. The regulation of tumor cell growth mediated by STAT3 and Pol I products was analyzed in vitro and in vivo. RNAseq, ChIP assays and rescue assays were used to uncover the mechanism of Pol I transcription mediated by STAT3. Results: STAT3 expression positively correlates with Pol I product levels and cancer cell growth. The inhibition of STAT3 or Pol I products suppresses cell growth. Mechanistically, STAT3 activates Pol I-directed transcription by enhancing the recruitment of the Pol I transcription machinery to the rDNA promoter. STAT3 directly activates Rpa34 gene transcription by binding to the RPA34 promoter, which enhances the occupancies of the Pol II transcription machinery factors at this promoter. Cancer patients with RPA34 high expression lead to poor survival probability and short survival time. Conclusion: STAT3 potentiates Pol I-dependent transcription and tumor cell growth by activating RPA34 in vitro and in vivo

Two-dimensional materials: synthesis and applications in the electro-reduction of carbon dioxide

The emission of CO2 has become an increasingly prominent issue. Electrochemical reduction of CO2 to value-added chemicals provides a promising strategy to mitigate energy shortage and achieve carbon neutrality. Two-dimensional (2D) materials are highly attractive for the fabrication of catalysts owing to their special electronic and geometric properties as well as a multitude of edge active sites. Various 2D materials have been proposed for synthesis and use in the conversion of CO2 to versatile carbonous products. This review presents the latest progress on various 2D materials with a focus on their synthesis and applications in the electrochemical reduction of CO2. Initially, the advantages of 2D materials for CO2 electro-reduction are briefly discussed. Subsequently, common methods for the synthesis of 2D materials and the role of these materials in the electrochemical reduction of CO2 are elaborated. Finally, some perspectives for future investigations of 2D materials for CO2 electro-reduction are proposed

Ligand-modulated nickel-catalyzed regioselective silylalkylation of alkenes

Abstract Organosilicon compounds have shown tremendous potential in drug discovery and their synthesis stimulates wide interest. Multicomponent cross-coupling of alkenes with silicon reagents is used to yield complex silicon-containing compounds from readily accessible feedstock chemicals but the reaction with simple alkenes remains challenging. Here, we report a regioselective silylalkylation of simple alkenes, which is enabled by using a stable Ni(II) salt and an inexpensive trans−1,2-diaminocyclohexane ligand as a catalyst. Remarkably, this reaction can tolerate a broad range of olefins bearing various functional groups, including alcohol, ester, amides and ethers, thus it allows for the efficient and selective assembly of a diverse range of bifunctional organosilicon building blocks from terminal alkenes, alkyl halides and the Suginome reagent. Moreover, an expedient synthetic route toward alpha-Lipoic acid has been developed by this methodology

Numerical analysis of single pad of thrust bearing with the energy equation solved by the characteristic-based split method

The solution of the energy equation of thermo-elasto-hydrodynamic analysis for bearings by the finite element method usually leads to convergence difficulties due to the presence of convection terms inherited from the Navier–Stokes equations. In this work, the numerical analysis is performed with finite element method universally by adopting the characteristic-based split method to solve the energy equation. Five case studies of fixed pad thrust bearings have been set up with different geometries, loads, and lubricants. The two-dimensional film pressure is obtained by solving the Reynolds equation with pre-defined axial load on the pad. The energy equation of the lubricant film and the heat transfer equation of the bearing pad are handled by characteristic-based split method and conventional finite element method in three-dimensional space, respectively. Hot oil carry-over effect and variable lubricant viscosity are considered in the simulations. The results of the temperature distributions in the lubricant film and the bearing pad are presented. The possible usability of characteristic-based split method for future thermo-elasto-hydrodynamic analysis is discussed

Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p 2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas

Research on Indoor Environment Prediction of Pig House Based on OTDBO–TCN–GRU Algorithm

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses

Classification of Plant Leaf Disease Recognition Based on Self-Supervised Learning

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection

Reductive soil disinfestation and Fe amendment improve soil microbial composition and Fritillaria production

The continuous obstacles of cropping cause severe economic loss, which seriously threaten agricultural sustainable development. In addition, managing excess waste, such as potato peel and mineral waste residues, is a vital burden for industry and agriculture. Therefore, we explored the feasibility of reductive soil disinfestation (RSD) with potato peel and amendment with iron mineral waste residues for the production of Fritillaria thunbergii, which is vulnerable to continuous obstacles. In this study, the influences of iron mineral, RSD with different organic maters, as well as the combined effects of iron mineral and RSD on Fritillaria rhizosphere soil physicochemical properties, microbial communities, and Fritillaria production were investigated. The results revealed that the RSD treatments with potato peel significantly reduced the soil salinity and increased the soil pH, microbial activity, organic matter, and the contents of K and Ca. RSD with potato peel also significantly thrived of the beneficial microbes (Bacillus, Azotobacter, Microvirga, and Chaetomium), and down-regulated potential plant pathogens. RSD with potato peel significantly promoted F. thunbergii yield and quality. Moreover, the combined effects of RSD and iron mineral amendment further enhanced soil health, improved microbial community composition, and increased the yield and peimisine content of F. thunbergii by 24.2% and 49.3%, respectively. Overall, our results demonstrated that RSD with potato peel and amendment with iron mineral waste residues can efficiently improve soil fertility, modify the microbial community, and benefit for both the sustainable production of F. thunbergii and the management of waste

Mixed Ligand Cu^IIN₂O₂ Complexes: Biomimetic Synthesis, Activities in Vitro and Biological Models, Theoretical Calculations

Three new mixed ligand Cu^IIN₂O₂ complexes, namely, [Cu^II(2-A-6-MBT)₂­(<i>m</i>-NB)₂] (<b>1</b>), [Cu^II(2-ABT)₂­(<i>m</i>-NB)₂] (<b>2</b>), and [Cu^II(2-ABT)₂­(<i>o</i>-NB)₂] (<b>3</b>), (2-A-6-MBT = 2-amino-6-methoxybenzothiazole, <i>m</i>-NB = <i>m</i>-nitrobenzoate, 2-ABT = 2-aminobenzothiazole, and <i>o</i>-NB = <i>o</i>-nitrobenzoate), have been prepared by the biomimetic synthesis strategy, and their structures were determined by X-ray crystallography studies and spectral methods. These complexes exhibited the effective superoxide dismutase (SOD) activity and catecholase activity. On the basis of the experimental data and computational studies, the structure–activity relationship for these complexes was investigated. The results reveal that electron-accepting abilities of these complexes and coordination geometries have significant effects on the SOD activity and catecholase activity. Then, we found that <b>1</b> and <b>2</b> exerted potent intracellular antioxidant capacity in the model of H₂O₂-induced oxidative stress based on HeLa cervical cancer cells, which were screened out by the cytotoxicity assays of different kinds of cells. Furthermore, <b>1</b>–<b>3</b> showed the favorable biocompatibility in two different biological models: <i>Saccharomyces cerevisiae</i> and human vascular endothelial cells. These biological experimental data are indicative of the promising application potential of these complexes in biology and pharmacology