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The use of molecularly modified electrodes in catalysis heralds a new paradigm in designing chemical transformations by allowing control of catalytic activity. Herein, we provide an overview of reported methods to develop electrodes functionalized with organometallic complexes and a summary of commonly used techniques for characterizing the electrode surface after immobilization. In addition, we highlight the implications of surface functionalization in catalysis to emphasize the key aspects that should be considered during the development and optimization of functionalized electrodes. Particularly, surface-molecule electronic coupling and electrostatic interactions within a hybrid system are discussed to present effective handles in tuning catalytic activity. We envision that this emerging type of hybrid catalytic system has the potential to combine the advantages of homogeneous catalysis and heterogeneous supports and could be applied to an expanded range of transformations beyond energy conversion.

Background: Anorectal malformations (ARMs) are among the most common congenital digestive tract malformations worldwide. Although there have been extensive efforts for elucidating their pathogenesis, the specific molecular mechanisms underlying ARMs remain unclear. Methods: We generated ARM Wistar rat models using ethylenethiourea. Nine pregnant rats were allocated to the ARM and control groups, respectively. Hindgut tissue was isolated from embryos collected on gestational days 14, 15, and 16, representing the key timepoints of anorectal development. High-throughput sequencing was used to identify differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the obtained genes. Module analysis was performed via Search Tool for the Retrieval of Interacting Genes (STRING), and a protein-protein interaction (PPI) network was constructed using Cytoscape software. Gene Set Enrichment Analysis (GSEA) was conducted to verify the enrichment of development/apoptosis/epithelium/morphogenesis/Wnt pathway-related core gene sets and identify ARM hub genes. Reverse transcription-quantitative polymerase chain reaction assay was employed to verify mRNA levels of hub genes. Results: 313 GSEA core genes form ten core gene sets were selected. Based on the 281 differentially expressed circRNAs (|Fc| > 2, FDR < 0.05), 42 PPI core genes were obtained. According to the integration of GSEA analysis and PPI network, three hub genes (Cul1, Gli3, and Osr2) highly associated with ETU-induced ARMs were identified. The qRT-PCR validation data were consistent with the sequencing results. Conclusions: These results provide a theoretical basis for the further identification of potential diagnostic and therapeutic targets in ARMs.

Local differential privacy (LDP) protects user information from potential threats by randomizing data on individual devices before transmission to untrusted collectors. This method enables collectors to derive user statistics by analyzing randomized data, thereby presenting a promising avenue for privacy-preserving data collection. In the context of key–value data, in which discrete and continuous values coexist, PrivKV has been introduced as an LDP protocol to ensure secure collection. However, this framework is susceptible to poisoning attacks. To address this vulnerability, we propose an expectation maximization (EM)-based algorithm combined with a cryptographic protocol to facilitate secure random sampling. Our LDP protocol, known as emPrivKV, exhibits two key advantages: it improves the accuracy of statistical information estimation from randomized data, and enhances resilience against the manipulation of statistics, that is, poisoning attacks. These attacks involve malicious users manipulating the analysis results without detection. This study presents the empirical results of applying the emPrivKV protocol to both synthetic and open datasets, highlighting a notable improvement in the precision of statistical value estimation and robustness against poisoning attacks. As a result, emPrivKV improved the frequency and the mean gains by 17.1% and 25.9%, respectively, compared to PrivKV, with the number of fake users being 0.1 of the genuine users. Our findings contribute to the ongoing discourse on refining LDP protocols for key–value data in scenarios involving privacy-sensitive information.

The effect of spatial heterogeneity on the microbial community and physicochemical properties during the primary fermentation of Daqu were investigated by high-throughput sequencing technology and conventional detection methods. Nongxiangxing baijiu Daqu inoculated with the unique ripe starter obtained by gradually culturing and expanding Daqu treated by cosmic rays was used. The results showed that although the intensity of change in driving factors varied among layer, their trends were the same. The liquefying, saccharifying and esterifying power of Daqu were higher in the bottom layer than in the upper and middle layers at the same fermentation time and the fluctuation was small. The microbial community of Daqu was composed of 12 dominant bacterial genera, including Lactobacillus, Weissella, Bacillus, Kosakonia, Staphylococcu and Thermoactinomyces, and seven dominant fungal genera, such as Pichia, Thermoascus, and Rhizomucor. Principal co-ordinates analysis and hierarchical clustering analysis showed significant differences in the bacterial and fungal community structure among fermentation stages and layers. Procrustes analysis and Mantel test showed that moisture had a significant effect on the bacterial community in Daqu, and acidity had a significant effect on the bacterial community in the middle and bottom layers of Daqu. Moreover, moisture had a significant effect on the fungal community in the upper and middle layers of Daqu. Redundancy analysis showed that moisture and acidity were positively correlated with Lactobacillus and Pichia, while driving factors had different influences on the microbial communities in different layers of Daqu. Therefore, the interaction and co-occurrence patterns of microbial genera in Daqu could change due to the differences in driving factors among different layers of Daqu. These results suggested that regulating driving factors during the Daqu making process is an effective way to improve the microbial community structure and quality of Daqu.

The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structure of the nuclear power plant system, its working mode and the association between each subsystem is carried out to analyze the monitoring parameters and fault characteristics and establish the fault data set. Secondly, an IFWA (Improved Fireworks Algorithm - Integrated Learning) algorithm is proposed to assess the severity of faults in the first circuit of a nuclear power plant. Finally, the fault diagnosis module is divided into three units according to the functional logic, i.e., condition monitoring unit, fault identification unit, and fault severity assessment unit. The results show that the diagnostic accuracy of the IFWA algorithm is 94.25% for SGTR in the single-fault diagnosis experiment and 96.25% for SGTR-LOCA in the multiple-fault diagnosis experiment. It shows that the IFWA algorithm proposed in this paper has the optimal performance capability when applied to nuclear power plant fault diagnosis and effectively assists managers in diagnosing faults and giving maintenance recommendations.