Search

Lignin, as an abundant organic carbon, plays a vital role in the global carbon cycle. However, our understanding of the global lignin‐degrading microbiome remains elusive. The greatest barrier has been absence of a comprehensive and accurate functional gene database. Here, we first developed a curated functional gene database (LCdb) for metagenomic profiling of lignin degrading microbial consortia. Via the LCdb, we draw a clear picture describing the global biogeography of communities with lignin‐degrading potential. They exhibit clear niche differentiation at the levels of taxonomy and functional traits. The terrestrial microbiomes showed the highest diversity, yet the lowest correlations. In particular, there were few correlations between genes involved in aerobic and anaerobic degradation pathways, showing a clear functional redundancy property. In contrast, enhanced correlations, especially closer inter‐connections between anaerobic and aerobic groups, were observed in aquatic consortia in response to the lower diversity. Specifically, dypB and dypA, are widespread on Earth, indicating their essential roles in lignin depolymerization. Estuarine and marine consortia featured the laccase and mnsod genes, respectively. Notably, the roles of archaea in lignin degradation were revealed in marine ecosystems. Environmental factors strongly influenced functional traits, but weakly shaped taxonomic groups. Null mode analysis further verified that composition of functional traits was deterministic, while taxonomic composition was highly stochastic, demonstrating that the environment selects functional genes rather than taxonomic groups. Our study not only develops a useful tool to study lignin degrading microbial communities via metagenome sequencing but also advances our understanding of ecological traits of these global microbiomes. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). Materials and methods: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. Results: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. Discussion: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. Conclusion: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence. [ABSTRACT FROM AUTHOR]

Information leakage and counterfeiting are serious worldwide issues that tremendously impact legitimate businesses and human life. Inspired by the Noctiluca scintillans, a new fluorescence‐afterglow dual‐modal information encryption enabled by precisely coordination‐modulated ultralong organic phosphorescence (UOP) is presented. Strikingly, the optical properties including fluorescence, lifetime and intensity of UOP can be precisely modulated on‐demand through energy transfer by lanthanide (LnIII) coordination, which enables information camouflage by similar LnIII luminescence to provide misleading information along with data decryption in the form of mutual afterglow. Moreover, the important data can be encrypted in a spatial‐time‐resolved way by programmatically coding information with afterglow gradients, yielding greatly improved security for verifying the authenticity. This study provides a new avenue to precisely modulate the optical properties of UOP materials and broadens the scope of optical materials for innovative information encryption and anticounterfeiting applications. [ABSTRACT FROM AUTHOR]

Purely organic room-temperature phosphorescence (RTP) materials have received intense attention due to their fascinating optical properties and advanced optoelectronic applications. The promotion of intersystem crossing (ISC) and minimalization of nonradiative dissipation under ambient conditions are key prerequisites for realizing high-performance organic RTP; However, the ISC process is generally inefficient for organic fluorogens and the populated triplet excitons are always too susceptible to be well stabilized by conventional means. Particularly, organizing organic fluorophores into compact and ordered entities by supramolecular dynamic interactions has proven to be a newly-emerged strategy to boost the ISC process greatly and suppress the non-radiative relaxations immensely, facilitating the population and stabilization of triplet excitons to access high-performance organic RTP. Consequently, well-defined organic emitters enable robust RTP emission even in the solution state, thus greatly extending the applications. Here, this review is focused on a timely and brief introduction to recent progress in tailoring ordered high-performance RTP emitters by supramolecular dynamic interactions. Their typical preparation strategies, optoelectronic properties, and applications are thoroughly summarized. In the summary section, key challenges and perspectives of this field are highlighted to suggest potential directions for future study., (© 2024 Wiley-VCH GmbH.)