Nitrogen-doped carbon nanospheres-modified graphitic carbon nitride with outstanding photocatalytic activity

Metals and metal oxides are widely used as photo/electro-catalysts for environmental remediation. However, there are many issues related to these metal-based catalysts for practical applications, such as high cost and detrimental environmental impact due to metal leaching. Carbon-based catalysts have the potential to overcome these limitations. In this study, monodisperse nitrogen-doped carbon nanospheres (NCs) were synthesized and loaded onto graphitic carbon nitride (g-C3N4, GCN) via a facile hydrothermal method for photocatalytic removal of sulfachloropyridazine (SCP). The prepared metal-free GCN-NC exhibited remarkably enhanced efficiency in SCP degradation. The nitrogen content in NC critically influences the physicochemical properties and performances of the resultant hybrids. The optimum nitrogen doping concentration was identified at 6.0 wt%. The SCP removal rates can be improved by a factor of 4.7 and 3.2, under UV and visible lights, by the GCN-NC composite due to the enhanced charge mobility and visible light harvesting. The mechanism of the improved photocatalytic performance and band structure alternation were further investigated by density functional theory (DFT) calculations. The DFT results confirm the high capability of the GCN-NC hybrids to activate the electron–hole pairs by reducing the band gap energy and efficiently separating electron/hole pairs. Superoxide and hydroxyl radicals are subsequently produced, leading to the efficient SCP removal

A Semi-Analytical Solution for the Thickness-Vibration of Centrally Partially-Electroded Circular AT-Cut Quartz Resonators

Vibration frequencies and modes for the thickness-shear vibrations of infinite partially-electroded circular AT-cut quartz plates are obtained by solving the two-dimensional (2D) scalar differential equation derived by Tiersten and Smythe. The Mathieu and modified Mathieu equations are derived from the governing equation using the coordinate transform and the collocation method is used to deal with the boundary conditions. Solutions of the resonant frequencies and trapped modes are validated by those results obtained from COMSOL software. The current study provides a theoretical way for figuring out the vibration analysis of circular quartz resonators

The Maize ABA Receptors ZmPYL8, 9, and 12 Facilitate Plant Drought Resistance

Drought is one of the major abiotic stresses affecting world agriculture. Breeding drought-resistant crops is one of the most important challenges for plant biologists. PYR1/PYL/RCARs, which encode the abscisic acid (ABA) receptors, play pivotal roles in ABA signaling, but how these genes function in crop drought response remains largely unknown. Here we identified 13 PYL family members in maize (ZmPYL1-13). Changes in expression of these genes under different stresses indicated that ZmPYLs played important roles in responding to multiple abiotic stresses. Transgenic analyses of ZmPYL genes in Arabidopsis showed that overexpression of ZmPYL3, ZmPYL9, ZmPYL10, and ZmPYL13 significantly enhanced the sensitivity of transgenic plants to ABA. Additionally, transgenic lines overexpressing ZmPYL8, ZmPYL9, and ZmPYL12 were more resistant to drought. Accumulation of proline and enhanced expression of drought-related marker genes in transgenic lines further confirmed the positive roles of ZmPYL genes in plant drought resistance. Association analyses with a panel of 368 maize inbred lines identified natural variants in ZmPYL8 and ZmPYL12 that were significantly associated with maize drought resistance. Our results deepen the knowledge of the function of maize PYL genes in responses to abiotic stresses, and the natural variants identified in ZmPYL genes may serve as potential molecular markers for breeding drought-resistant maize cultivars

The Maize Clade A PP2C Phosphatases Play Critical Roles in Multiple Abiotic Stress Responses

As the core components of abscisic acid (ABA) signal pathway, Clade A PP2C (PP2C-A) phosphatases in ABA-dependent stress responses have been well studied in Arabidopsis. However, the roles and natural variations of maize PP2C-A in stress responses remain largely unknown. In this study, we investigated the expression patterns of ZmPP2C-As treated with multiple stresses and generated transgenic Arabidopsis plants overexpressing most of the ZmPP2C-A genes. The results showed that the expression of most ZmPP2C-As were dramatically induced by multiple stresses (drought, salt, and ABA), indicating that these genes may have important roles in response to these stresses. Compared with wild-type plants, ZmPP2C-A1, ZmPP2C-A2, and ZmPP2C-A6 overexpression plants had higher germination rates after ABA and NaCl treatments. ZmPP2C-A2 and ZmPP2C-A6 negatively regulated drought responses as the plants overexpressing these genes had lower survival rates, higher leaf water loss rates, and lower proline accumulation compared to wild type plants. The natural variations of ZmPP2C-As associated with drought tolerance were also analyzed and favorable alleles were detected. We widely studied the roles of ZmPP2C-A genes in stress responses and the natural variations detected in these genes have the potential to be used as molecular markers in genetic improvement of maize drought tolerance

HGSMDA: miRNA–Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss

Biological research has demonstrated the significance of identifying miRNA–disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA–disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases

Modeling of an Opposed Multiburner Gasifier with a Reduced-Order Model

A reduced-order model (ROM) is considered a promising solution for engineering simulation of a gasifier. In this study, a ROM of a commercial-scale opposed multiburner gasifier is developed based on a reactor network model (RNM). The RNM blocking for this gasifier is established and validated based on analysis of the gasifier flow field. The particle flow in the gasifier is characterized by the particle residence time in each reactor of the RNM. The random pore model and the “effective factor” method are employed to model the char gasification rate under high pressure and temperature. The interphase heat transfer and heat loss through the refractory wall are calculated. In model validation, the simulation results show well agreement with the industrial data. The model provides distributions of the gas temperature and compositions in the gasifier. Effects of the particle size on the particle temperature and carbon conversion are discussed quantitatively. It is observed that fine particles can be completely converted in the jet zone, while the large ones (>100 μm) are converted mainly in the impinging zone and impinging flow zone

Application of Spectroscopic Characteristics of White Mica in Porphyry Tungsten Deposits: A Case Study Involving the Shimensi Deposit in Northern Jiangxi

Shortwave infrared (SWIR) technology is characterized by high efficiency and convenience and is widely used in the mineral exploration of porphyry, epithermal, and skarn types. However, studies on the SWIR spectral features of porphyry tungsten deposits are still lacking. The Dahutang tungsten deposit has reached an ultra large scale, characterized by the porphyry type. Based on the SWIR spectral features of white mica and its petrographic, geochemical, and Raman spectral features, this paper discusses the use of shortwave infrared and Raman spectral features and major and trace element contents in white mica for exploration of the Shimensi mine in Dahutang. The results showed that the SWIR wavelength of the single-frequency Al-O-H absorption peak position (Pos2200) of white micas in ore-bearing intrusions were over 2209 nm; the Raman shift of aluminium atom bridge-bonds (Al, O (br)) were mainly located between 410 and 420 cm−1. The contents of Si, Fe, and Mg were relatively high; the contents of Al, Na, and K were low; and the variation of the Nb/Ta value reflected the magmatic evolution degree. The shift of Pos2200 of white mica showed a correlation with the Raman spectral features and contents of Si, Al, and other elements. This study shows that the SWIR spectral features of white mica were useful for further exploration of the Shimensi area in Dahutang and provided a potential tool for the exploration of porphyry tungsten deposits