Acid-Triggered Self-Assembled Egg White Protein-Coated Gold Nanoclusters for Selective Fluorescent Detection of Fe\u3csup\u3e3+\u3c/sup\u3e, NO2\u3csup\u3e-\u3c/sup\u3e, and Cysteine

Herein, we present a simple and economical synthesis for the first multianalyte probe able to selectively quantify the concentrations of Fe3+, NO2-, and cysteine. It comprises H+-triggered self-assembled gold nanoclusters (AuNCs@EW/H+, AuEHs), showing enhanced red fluorescence at 640 nm. The AuEH is a good fluorescent nanosensor for Fe3+ and NO2- with detection limits of 1.40 and 2.82 nM, respectively. Iron detection, through fluorescence quenching, occurs because of nanocluster aggregation elicited by the complexation of Fe3+ with amino acids on the surface of AuEH; nitrite detection likely proceeds through fluorescence quenching via the disassembly of the nanoclusters following irreversible oxidation by nitrite. This selectivity is good enough that it can be used to quantify the nitrite concentration in commercially available processed meat. Cysteine detection occurs through the restoration of fluorescence of iron-quenched samples; similar molecules including homocysteine and glutathione are unable to restore fluorescence, showing the specificity of the interaction. Applications, including as a detecting ink and as a biocompatible probe, show promise because of the lack of observable toxicity of the AuEHs, demonstrating their promise as specific and sensitive biosensors

Evaluation of the Temporal and Spatial Changes of Ecological Quality in the Hami Oasis Based on RSEI

Given the restrictions on special geographic locations in development processes, the measurement and analysis of the ecological quality of the Hami Oasis are of great significance for the protection of this fragile oasis. In this study, the ecological quality of the Hami Oasis was monitored by constructing a remote sensing ecological index (RSEI) for arid areas. Using the standard deviation ellipse and moving window method, the ecological status and space–time changes were explored for both their external and internal factors in the Hami Oasis. Finally, a geo-detector was employed to determine the driving factors of the ecological quality of the Hami Oasis. The results revealed that: (1) In the remote sensing ecological index constructed in the Hami Oasis, the main influencing factors were dryness and wetness. The average value of the ecological quality of the oasis was less than 0.5, and the ecological quality level was relatively poor. Among the five grades of ecological quality in the Hami Oasis, the poor grade and the good grade showed the largest changes, decreasing by 200 and increasing by 300, respectively, which were mainly concentrated in the periphery of the oasis. (2) The improved ecological quality of the Hami Oasis was mainly manifested in the expansion of the artificial oasis, while the deteriorated area was manifested as an increase in the built-up area. Moreover, the ecological quality of the Hami Oasis presented a ringlike nesting distribution pattern from the internal built-up area to the artificial oasis periphery. (3) The external expansion direction of the ecological quality of the Hami Oasis featured southeast–northwest expansion, which was consistent with the direction of the rivers and traffic roads. The transformation between different ecological qualities in the oasis and the expansion of the built-up area were the reasons for the fragmentation of the Hami Oasis’ landscape. (4) Compared to a single factor, the dual-factor for the ecological quality of the Hami Oasis had stronger explanatory power. Moreover, changes in land use types caused changes in the ecological quality of the Hami Oasis. During the study period, we found that human activities had a more significant impact than natural factors on the development of the Hami Oasis. (5) The Moran’s I Index increased from 0.835268 in 2000 to 0.923976 in 2018, and the p values in the study area all reached a 0.05 significant level. At the same time, the areas with p values above the 0.01 and 0.001 significant levels have also increased significantly in the past 18 years

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning.

The past decade witnessed rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve the classification and prediction of these properties. However, the barriers to collecting large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, advanced approaches (AL, SSL, and the hybrid) can greatly reduce the number of labeled samples, with some cases decreasing the number of labeled samples by more than half

An integrated strategy of UPLC-Q-TOF-MS analysis, network pharmacology, and molecular docking to explore the chemical constituents and mechanism of Zixue Powder against febrile seizures

Febrile seizures (FS) are the most common type of seizures for children. As a commonly used representative cold formula for resuscitation, Zixue Powder (ZP) has shown great efficacy for the treatment of FS in clinic, while its active ingredients and underlying mechanism remain largely unclear. This study aimed to preliminarily elucidate the material basis of ZP and the potential mechanism for the treatment of FS through ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), network pharmacology, and molecular docking. UPLC-Q-TOF-MS was firstly applied to characterize the ingredients in ZP, followed by network pharmacology to explore the potential bioactive ingredients and pathways of ZP against FS. Furthermore, molecular docking technique was employed to verify the binding affinity between the screened active ingredients and targets. As a result, 75 ingredients were identified, containing flavonoids, chromogenic ketones, triterpenes and their saponins, organic acids, etc. Through the current study, we focused on 13 potential active ingredients and 14 key potential anti-FS targets of ZP, such as IL6, STAT3, TNF, and MMP9. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that inflammatory response, EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications, and neuroactive ligand-receptor interaction were the main anti-FS signaling pathways. Licochalcones A and B, 26-deoxycimicifugoside, and hederagenin were screened as the main potential active ingredients by molecular docking. In conclusion, this study provides an effective in-depth investigation of the chemical composition, potential bioactive components, and possible anti-FS mechanism of ZP, which lays the foundation for pharmacodynamic studies and clinical applications of ZP

A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future