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Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep learning (OSDL) with single-cell Raman spectroscopy to identify pathogens in real-world air containing diverse unknown indigenous bacteria that cannot be fully included in training sets. To test and further enhance identification, we constructed the Raman datasets of aerosolized bacteria. Through optimizing OSDL algorithms and training strategies, Raman-OSDL achieves 93% accuracy for five target airborne pathogens, 84% accuracy for untrained air bacteria, and 36% reduction in false positive rates compared to conventional close-set algorithms. It offers a high detection sensitivity down to 1:1000. When applied to real air containing >4600 bacterial species, our method accurately identifies single or multiple pathogens simultaneously within an hour. This single-cell tool advances rapidly surveilling pathogens in complex environments to prevent infection transmission. [ABSTRACT FROM AUTHOR]

Abstract The apelin receptor (APJR) emerges as a promising drug target for cardiovascular health and muscle regeneration. While prior research unveiled the structural versatility of APJR in coupling to Gi proteins as a monomer or dimer, the dynamic regulation within the APJR dimer during activation remains poorly understood. In this study, we present the structures of the APJR dimer and monomer complexed with its endogenous ligand apelin-13. In the dimeric structure, apelin-13 binds exclusively to one protomer that is coupled with Gi proteins, revealing a distinct ligand-binding behavior within APJR homodimers. Furthermore, binding of an antagonistic antibody induces a more compact dimerization by engaging both protomers. Notably, structural analyses of the APJR dimer complexed with an agonistic antibody, with or without Gi proteins, suggest that G protein coupling may promote the dissociation of the APJR dimer during activation. These findings underscore the intricate interplay between ligands, dimerization, and G protein coupling in regulating APJR signaling pathways.

Abstract Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the “specimen size effect.” Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 55 columns including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens.

River ecosystems are vital for maintaining ecosystem balance but rapid development in China’s Yangtze River Basin has led to severe ecological degradation. Therefore, this study aimed to evaluate the aquatic ecosystem health of the Jialing River Basin, investigate the influencing factors of the aquatic ecology, and predict the succession trend of the phytoplankton community. Phytoplankton was collected in the Jialing River (Nanchong section) and one of its tributaries (Xichong River). Morphological identification methods, random forest, neutral community model, and modified stochasticity ratio were used to analyze phytoplankton composition, environmental drivers, and community assembly mechanisms. The results showed that 1) Bacillariophyta, Cryptophyta, and Chlorophyta were the dominant phyla in the Jialing River, followed by Cyanobacteria. The structure of the phytoplankton community was affected by seasonal changes and differences in the tributary and mainstem. 2) Overall, the autumn phytoplankton community α-diversity index was significantly higher than that of spring (P

Co-exposure to ground-level ozone (O3) and fine particles (PM2.5, ≤ 2.5 µm in diameter) has become a primary scenario for air pollution exposure of urbanites in China. Recent studies have suggested a synergistic effect of PM2.5 and O3 on induction of lung inflammatory injury. However, the underlying mechanisms for respiratory toxicity induced by this co-exposure have not been adequately elucidated. In this study, a realistic exposure was based to set up the co-exposure condition of an animal model. Specifically, eighty male C57BL/6 mice (10 months old) were randomly divided into four groups: control, O3, PM2.5 and co-exposure (O3 + PM2.5). Mice in the co-exposure group breathed O3 and orally inhaled PM2.5 suspension. The scenario for O3 exposure was 0.6 ppm, 4 h/d, for 30 consecutive days while that for PM2.5 exposure was oral inhalation of PM2.5 suspension (5.6 mg/kg bw) once every other day and 4 h prior to O3 exposure. After last exposure, bronchoalveolar lavage fluids (BALF) were collected for inflammatory biomarker measurement, 16S rRNA sequencing and metabolite profiling. Lung tissues were processed for histological examination. The results demonstrated that co-exposure to O3 and PM2.5 exacerbated the pathological changes and inflammatory response induced by O3 or PM2.5. Further studies revealed that co-exposure to O3 and PM2.5 increased the abundance of Prevotella in the airways and caused more severe metabolic disorders compared to O3 or PM2.5 exposure. Spearman correlation analysis demonstrated correlations among airway microbiota dysbiosis, metabolic disorder, inflammation, and pathological alterations induced by co-exposure to O3 and PM2.5. In summary, co-exposure to O3 and PM2.5 worsens airway inflammatory injury, possibly through interrelated airway microbiota dysbiosis and metabolic disorder.

Background: Recent research suggests that the emerging neutrophil-albumin ratio (NAR) has a significant correlation with the survival outcomes across a range of tumors, yet its predictive significance for nasopharyngeal carcinoma (NPC) remains insufficiently investigated. This study aimed to evaluate the relationship between the neutrophil-to-albumin ratio (NAR) and overall survival (OS) in patients with NPC, as well as to develop a corresponding prognostic model. Methods: This retrospective analysis included 861 NPC patients treated with concurrent chemoradiotherapy (CCRT), who were randomly divided into a training group (n = 605) and a validation group (n = 256). To identify factors associated with OS and construct a prognostic nomogram, both univariate and multivariate Cox regression analyses were performed. The nomogram's prognostic accuracy was evaluated and independently validated. Results: The NAR score successfully segregated NPC patients into two categories with significantly different OS (HR = 0.536; 95 % CI: 0.296–0.972, P = 0.040). Through multivariate analysis, factors such as age, T stage, N stage, and NAR score were identified as independent predictors of OS, leading to the creation of a prognostic nomogram. This nomogram demonstrated superior predictive capability for OS [C-index = 0.702 (95 % CI: 0.636–0.768)], surpassing that of the conventional staging system [C-index = 0.651 (95 % CI: 0.549–0.752)]. The findings underwent internal validation within an independent cohort. Conclusions: The NAR, an emergent biomarker combining nutritional and inflammatory status, offers a practical, low-cost, and non-invasive prognostic measure for NPC patients treated with CCRT. Additionally, the prognostic nomogram derived from NAR surpasses traditional staging systems in predictive accuracy.

The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage during information sharing, or they suffer from insufficient information sharing while protecting data privacy, leading to suboptimal forecasting performance. To address these issues, this paper proposes a privacy-preserving deep federated learning method for the probabilistic forecasting of ultra-short-term power generation from DPV systems. Firstly, a collaborative feature federated learning framework is established. For the central server, information sharing among clients is realized through the interaction of global models and features while avoiding the direct interaction of raw data to ensure the security of client data privacy. For local clients, a Transformer autoencoder is used as the forecasting model to extract local temporal features, which are combined with global features to form spatiotemporal correlation features, thereby deeply exploring the spatiotemporal correlations between different power stations and improving the accuracy of forecasting. Subsequently, a joint probability distribution model of forecasting values and errors is constructed, and the distribution patterns of errors are finely studied based on the dependencies between data to enhance the accuracy of probabilistic forecasting. Finally, the effectiveness of the proposed method was validated through real datasets.