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The presence of bacteria and viruses in water has caused many large-scale water disease outbreaks. Cold atmospheric plasma has received much attention as an emerging non-thermal purification technology capable of eliminating micro-organisms, non-ferrous fuels, and heavy metals from water. In this study, a novel gas–liquid diaphragm discharge device that generates air plasma driven by a sinusoidal AC power supply was designed, and its sterilizing effect was investigated. First, the concentrations of reactive oxygen and nitrogen species in the diaphragm discharge were characterized, and the physicochemical properties of activated water were explored. Next, experiments on the inactivation of Pseudomonas aeruginosa were carried out using the device. Finally, pond sewage was treated with the device, and the diversity of micro-organisms in the water was analyzed using 16S rRNA assays to further investigate its applicability. The results showed that the species and quantities of micro-organisms in the water significantly reduced after discharge treatment, with the most significant changes occurring in the Bacillus and Firmicutes species. Thus, this study provides an alternative method for microbial inactivation in sewage treatment. [ABSTRACT FROM AUTHOR]

Chiral carbon nanodots (CNDs) were fabricated through the hydrothermal processing of sulfanilic acid and chiral tartaric acid, exhibiting outstanding catalytic performance for the chiral catalysis of the ring-opening reaction. Furthermore, the catalytic mechanism was proposed to understand the link between the chiral structure and the performance of the catalyst. [ABSTRACT FROM AUTHOR]

Engineering enzyme–substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein–ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a precision of ≥50% and 52%, 47.6%, and 58.1%. The distance of predicted and real center is ≤4 Å, which is based on SC6K, COACH420, and BU48 validation datasets. The substrate binding sites of Klebsiella variicola acid phosphatase (KvAP) and Bacillus anthracis proline 4-hydroxylase (BaP4H) were predicted using DUnet, showing high competitive performance of 53.8% and 56% of the predicted binding sites that critically affected the catalysis of KvAP and BaP4H. Virtual saturation mutagenesis was applied based on the predicted binding sites of KvAP, and the top-ranked 10 single mutations contributed to stronger enzyme–substrate binding varied while the predicted sites were different. The advantage of DUnet for predicting key residues responsible for enzyme activity further promoted the success rate of virtual mutagenesis. This study highlighted the significance of correctly predicting key binding sites for enzyme engineering. [ABSTRACT FROM AUTHOR]

Inherited retinal degenerations (IRDs) are a group of genetic disorders characterized by the progressive degeneration of retinal cells, leading to irreversible vision loss. SLC4A7 has emerged as a candidate gene associated with IRDs, yet its mechanisms remain largely unknown. This study aims to investigate the role of slc4a7 in retinal development and its associated molecular pathogenesis in zebrafish. Morpholino oligonucleotide knockdown, CRISPR/Cas9 genome editing, quantitative RT-PCR, eye morphometric measurements, immunofluorescent staining, TUNEL assays, visual motor responses, optokinetic responses, rescue experiments, and bulk RNA sequencing were used to assess the impact of slc4a7 deficiency on retinal development. Our results demonstrated that the knockdown of slc4a7 resulted in a dose-dependent reduction in eye axial length, ocular area, and eye-to-body-length ratio. The fluorescence observations showed a significant decrease in immunofluorescence signals from photoreceptors and in mCherry fluorescence from RPE in slc4a7-silenced morphants. TUNEL staining uncovered the extensive apoptosis of retinal cells induced by slc4a7 knockdown. Visual behaviors were significantly impaired in the slc4a7-deficient larvae. GO and KEGG pathway analyses reveal that differentially expressed genes are predominantly linked to aspects of vision, ion channels, and phototransduction. This study demonstrates that the loss of slc4a7 in larvae led to profound visual impairments, providing additional insights into the genetic mechanisms predisposing individuals to IRDs caused by SLC4A7 deficiency. [ABSTRACT FROM AUTHOR]

Dielectric capacitors, which can achieve tremendous power density and ultrafast charge/discharge speed, are crucial components for high power equipment. Yet, the synchronous achievement in high recoverable energy density (Wrec) and high efficiency (η) is a long‐term difficulty for dielectric ceramics. Herein, the (0.85 − x)NaNbO3–0.15BaTiO3–xBi(Mg1/3Ta2/3)O3 (NN–BT–xBMT) relaxor ferroelectric ceramics were proposed and studied. The doping of BMT was conducive to promoting the microstructure and bandgap and improving the dielectric relaxation properties of NN–BT ceramics. Finally, the sample of x = 0.10 presented a high Wrec of 5.9 J/cm3 and a high η of 90% simultaneously. In addition, the sample also exhibited superb charge/discharge performances with large power density (PD = 137 MW/cm3) and fast charge/discharge speed (t0.9 = 39 ns). The above results reveal that the NaNbO3‐based ceramics could serve as potential alternatives for advanced dielectric capacitors. [ABSTRACT FROM AUTHOR]

Study Objective. Sugammadex is a widely used neuromuscular blockade reversal agent. Cost concerns limit use in pediatric patients for, whom recommended doses are often far less than vial quantities. Therefore, we undertook a quality improvement initiative at a pediatric hospital in which sugammadex vials were fractionated. We hypothesized we would see a decrease in administered dose and waste and improved adherence with dosing guidelines. Methods. Sugammadex vials were fractionated into 25, 50, or 100 mg aliquots, which would be distributed to anesthesia staff by pharmacy staff in approximate 2 mg/kg of actual body weight doses (±10%). We analyzed changes in sugammadex waste and dosing practices 1/1/2019 to 3/15/2023 pre/postintervention (4/1/2021). We gauged dose appropriateness using last train of four (TOF) prior to sugammadex administration. Results. 7,889 patients 2–17 years (4,771 with documented TOF), ASA 1–4 receiving general anesthesia with a steroidal NMB medication and sugammadex reversal. Pre‐ and postintervention mean doses were 2.5 mg/kg (SD: 1.2) and 2.4 mg/kg (SD: 0.96), respectively. A smaller proportion of cases received standard 2 or 4 mg/kg doses (pre: 77.6 vs. post: 66.7%). Mean waste per case declined from 4.2 mg/kg (SD: 4.1) to 0.22 mg/kg (SD: 0.38). Among cases with 0 or 1 measured twitches on TOF that should receive at least 4 mg/kg, fewer received at least 3.6 mg/kg (post: 56.7% vs. pre: 66.8%), and a greater proportion received less than 2.2 mg/kg (post: 27.4% vs. pre: 20.7%). Among cases that should have received at least 2 mg/kg by TOF, the proportion of patients receiving more than 3.6 mg/kg declined from 9.5% to 5.2%. Discussion. Fractionating sugammadex vials was associated with decreases in waste, but not dose, and significant underdosing was more likely to occur. While vial fractionation could enable increased access to sugammadex and other costly medications, it may introduce unintended consequences. [ABSTRACT FROM AUTHOR]

In the context of low-pressure casting, an excessive inlet velocity may result in the introduction of an oxide film and air into a liquid metal, leading to the formation of a two-layer film structure within the casting. Such defects can significantly degrade the mechanical properties of the castings. In order to optimize the advantages of low-pressure casting, an empirically designed equation for the inlet velocity was formulated and the concept of critical inlet velocity was further refined. A comprehensive numerical simulation was conducted to meticulously analyze the liquid metal spreading phase within the cavity. Subsequently, low-pressure casting experiments were carried out with actual castings of an A357 alloy, using two different entrance velocities—one critical and the other exceeding the critical entrance velocity. Tensile test specimens were extracted from the castings for the comparative evaluation of mechanical properties. It was observed that the average tensile strength of specimens cast at the critical inlet velocity exhibited a notable 16% enhancement. In contrast, specimens cast at velocities exceeding the critical inlet velocity manifested the presence of double oxide film defects. This evidence suggests that casting at a velocity faster than the critical inlet velocity leads to the formation of double oxide film defects, which in turn reduces the mechanical properties of the castings. [ABSTRACT FROM AUTHOR]

Refractory high-entropy alloys (RHEAs) typically exhibit excellent high-temperature strength but limited ductility. In this study, a comprehensive machine learning strategy with integrated material knowledge is proposed to predict the elongation of TiZrNbVAl RHEAs. By referring to the ductility theories, a set of cost-effective material features is developed with various mathematical forms of thermodynamic parameters. These features are proven to effectively incorporate material knowledge into ML modeling. They also offer potential alternatives to those obtained from costly first-principles calculations. Based on Pearson correlation coefficients, the linear relationships between pairwise features were compared, and the seven key features with the greatest impact on the model were selected for ML modeling. Regression tasks were performed to predict the ductility of TiZrNbVAl, and the CatBoost gradient boosting algorithm exhibiting the best performance was eventually selected. The established optimized model achieves high predictive accuracies exceeding 0.8. These key features were further analyzed using interpretable ML methods to elucidate their influences on various ductility mechanisms. According to the ML results, different compositions of TiZrNbVAl with excellent tensile properties were prepared. The experimental results indicate that Ti44Zr24Nb17V5Al10 and Ti44Zr26Nb8V13Al9 both exhibited ultimate tensile strengths of approximately 1180 MPa and elongations higher than 21%. They verified that the ML strategy proposed in this study is an effective approach for predicting the properties of RHEAs. It is a potential method that can replace costly first-principles calculations. Thermodynamic parameters have been shown to effectively predict alloy ductility to a certain extent. [ABSTRACT FROM AUTHOR]