Degradation and regeneration inhibition of PCDD/Fs in incineration fly ash by low-temperature thermal technology.

Low-temperature thermal degradation of PCDD/Fs for incineration fly ash (IFA), as a novel and emerging technology approach, offers promising features of high degradation efficiency and low energy consumption, presenting enormous potential for application in IFA resource utilization processes. This review summarizes the concentrations, congener distributions, and heterogeneity characteristics of PCDD/Fs in IFA from municipal, medical, and hazardous waste incineration. A comparative analysis of five PCDD/Fs degradation technologies is conducted regarding their characteristics, industrial potential, and applicability. From the perspective of low-temperature degradation mechanisms, pathways to enhance PCDD/Fs degradation efficiency and inhibit their regeneration reactions are discussed in detail. Finally, the challenges to achieve low-temperature degradation of PCDD/Fs for IFA with high-efficiency are prospected. This review seeks to explore new opportunities for the detoxification and resource utilization of IFA by implementing more efficient and viable low-temperature degradation technologies.

Selective autophagy receptor p62/SQSTM1 inhibits TBK1-IRF7 innate immune pathway in triploid hybrid fish.

The production of type I interferon is tightly regulated to prevent excessive immune activation. However, the role of selective autophagy receptor SQSTM1 in this regulation in teleost remains unknown. In this study, we cloned the triploid fish SQSTM1 (3nSQSTM1), which comprises 1371 nucleotides, encoding 457 amino acids. qRT-PCR data revealed that the transcript levels of SQSTM1 in triploid fish were increased both in vivo and in vitro following spring viraemia of carp virus (SVCV) infection. Immunofluorescence analysis confirmed that 3nSQSTM1 was mainly distributed in the cytoplasm. Luciferase reporter assay results showed that 3nSQSTM1 significantly blocked the activation of interferon promoters induced by 3nMDA5, 3nMAVS, 3nTBK1, and 3nIRF7. Co-immunoprecipitation assays further confirmed that 3nSQSTM1 could interact with both 3nTBK1 and 3nIRF7. Moreover, upon co-transfection, 3nSQSTM1 significantly inhibited the antiviral activity mediated by TBK1 and IRF7. Mechanistically, 3nSQSTM1 decreased the TBK1 phosphorylation and its interaction with 3nIRF7, thereby suppressing the subsequent antiviral response. Notably, we discovered that 3nSQSTM1 also interacted with SVCV N and P proteins, and these viral proteins may exploit 3nSQSTM1 to further limit the host's antiviral innate immune responses. In conclusion, our study demonstrates that 3nSQSTM1 plays a pivotal role in negatively regulating the interferon signaling pathway by targeting 3nTBK1 and 3nIRF7.

NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design.

Machine learning techniques have significantly transformed the way materials scientists conduct research. However, the widespread deployment of machine learning software in daily experimental and simulation research for materials and chemical design has been limited. This is partly due to the substantial time investment and learning curve associated with mastering the necessary codes and computational environments. In this paper, we introduce a user-friendly, data-driven machine learning interface featuring multiple "button-clicking" functionalities to streamline the design of materials and chemicals. This interface automates the processes of transforming materials and molecules, performing feature selection, constructing machine learning models, making virtual predictions, and visualizing results. Such automation accelerates materials prediction and analysis in the inverse design process, aligning with the time criteria outlined by the Materials Genome Initiative. With simple button clicks, researchers can build machine learning models and predict new materials once they have gathered experimental or simulation data. Beyond the ease of use, NJmat offers three additional features for data-driven materials design: (1) automatic feature generation for both inorganic materials (from chemical formulas) and organic molecules (from SMILES), (2) automatic generation of Shapley plots, and (3) automatic construction of "white-box" genetic models and decision trees to provide scientific insights. We present case studies on surface design for halide perovskite materials encompassing both inorganic and organic species. These case studies illustrate general machine learning models for virtual predictions as well as the automatic featurization and Shapley/genetic model construction capabilities. We anticipate that this software tool will expedite materials and molecular design within the scope of the Materials Genome Initiative, particularly benefiting experimentalists.

Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles.

Directly applying big language models for material and molecular design is not straightforward, particularly for real-scenario cases, where experimental validation accuracy is required. In this study, we propose a multimode descriptor design method for materials prediction and analysis, leveraging the advantages of the natural language processing literature model and density functional theory (DFT) calculations with the assistance of the genetic algorithm (GA). A case study on prediction of aqueous photocurrents of multisolvent engineered halide perovskite CH3NH3PbI3 is performed, and the following-up validation experiments are carried out to demonstrate the improved accuracy of the multimode descriptors (an unprecedented experimental validation accuracy of 87.5% via the GA is achieved) for predicting aqueous photocurrents of perovskite materials (c.f. only 50% experimental accuracy for other common machine learning models). The improved experimental accuracy of the descriptors is attributed to the successful deployment of a language model incorporating concise scientific information from >1 million articles into molecular descriptors in combination with DFT calculations. The subsequent machine learning analysis suggests the importance of cation···π and crystallization in molecule-modified halide perovskite materials representing ontological and conceptual understanding. Importantly, the genetic process affords an accurate "white-box" model to describe the perovskite stability (accuracy = 90.2% for the test data set and 92.3% for the train data set) with the mathematical equation Stability=tanF2×F3×F1F2+F4+F5, where F1 ∼ F5 atomic-level structural and chemical details such as cation···π interactions and highest occupied molecular orbital levels. This study offers a feasible descriptor design route to accurately predict complex material properties, leveraging both language models and density functional theories.

Collaborative optimization of depot location, capacity and rolling stock scheduling considering maintenance requirements.

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed.

Developing geriatric nursing micro-credentials for undergraduate nursing students based on training objectives: A modified Delphi study.

BACKGROUND: China's population has begun to age rapidly in the past several years and this trend is predicted to continue. In the face of this growing older population, the existing number of aged care personnel, especially medical care professionals, can hardly meet the demand for aged care services. AIM: To develop geriatric nursing micro-credentials (MCs) for undergraduate nursing students based on standardized training objectives and to specify the learning goals and course modules that correspond to each specific MC. DESIGN: Modified Delphi study. METHODS: An initial set of geriatric nursing MCs were developed based on the training objectives. Expert group discussion (n=13) reviewed the clarity and intelligibility of the statements' wording and supplemented the framework. A three-round Delphi survey (n=15) was then employed to obtain a consensus on the learning goals and course modules via an online questionnaire. Descriptive statistics were used to analyze the data. RESULTS: The final geriatric nursing MCs consisted of six courses, namely fundamentals of geriatric nursing (8 modules), geriatric sociology (6 modules), geriatric clinical nursing (3 modules), geriatric psychological nursing (8 modules), geriatric rehabilitation nursing (8 modules) and geriatric hospice care (10 modules). CONCLUSION: Nursing faculty can use the geriatric nursing MCs developed in this study to train current undergraduate nursing students to become backups for current, fully credentialed geriatric caregivers.

The domestic elder abuse in China: Scale development and psychometric properties.

An effective screening tool is essential to elder abuse research. Although several instruments have been developed in China to measure elder abuse, they present several limitations. The instrument development involved three components: (1) generating questionnaire items; (2) questionnaire testing and data collection in older adults; and (3) psychometric evaluation of the Domestic Elder Abuse Scale (DEAS). We collected questionnaire responses from 3725 community-dwelling Chinese older adults. The 26-item DEAS showed good reliability and validity across five dimensions: physical abuse, psychological abuse, financial exploitation, neglect, and abandonment. These five factors accounted for 78.432 % of the total variance, and model fitting results were acceptable. The Cronbach's alpha coefficient of the scale was 0.975, and the test-retest intraclass correlation coefficient (ICC) was 0.934 after 2 weeks. This study developed a five-dimension instrument to measure elder abuse, with good psychometric properties, which can play an essential role in community-based studies in China.