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The human eye, which relies on a flexible and controllable lens to focus light onto the retina, has inspired many scientific researchers to understand better and imitate the biological vision system. However, real-time environmental adaptability presents an enormous challenge for artificial eye-like focusing systems. Inspired by the mechanism of eye accommodation, we propose a supervised-evolving learning algorithm and design a neuro-metasurface focusing system. Driven by on-site learning, the system exhibits a rapid response to ever-changing incident waves and surrounding environments without any human intervention. Adaptive focusing is achieved in several scenarios with multiple incident wave sources and scattering obstacles. Our work demonstrates the unprecedented potential for real-time, fast, and complex electromagnetic (EM) wave manipulation for various purposes, such as achromatic, beam shaping, 6 G communication, and intelligent imaging. Here the authors propose for the first time the concept of supervised-evolving learning (SEL) and a corresponding SEL-driven adaptive focusing (SELAF) system. This metasurface can adaptively realize focusing at any specified position for waves incident from any direction. This work demonstrates unprecedented potential for tasks involving real-time, fast and complex electromagnetic wave manipulation.

At present, the method of using coupled models to model different frequency subseries of precipitation series separately for prediction is still lacking in the research of precipitation prediction, thus in this paper, a coupled model based on Ensemble Empirical Mode Decomposition (EEMD), Long Short-Term Memory neural network (LSTM) and Autoregressive Integrated Moving Average (ARIMA) is proposed for month-by-month precipitation prediction. The monthly historical precipitation data of Luoyang City from 1973 to 2021 were used to build the model, and the modal components of different frequencies obtained by EEMD decomposition were divided into high-frequency series part and low-frequency series part using the Permutation Entropy (PE) algorithm, the LSTM model is used to predict the high-frequency sequence part, while the ARIMA model is used to predict the low-frequency sequence part. Monthly precipitation forecasts are obtained by superimposing the results of the two models. Finally, the predictive performance is evaluated using several assessment metrics. The indicators show that the model predictive performance outperforms the EMD-LSTM (Empirical Mode Decomposition), EEMD-LSTM, EEMD-ARIMA combined models and the single models, and the model has high confidence in the prediction results of future precipitation.

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity and non-stationarity characteristics of gate-front water level sequences, this paper introduces a gate-front water level forecasting method based on a GRU–TCN–Transformer coupled model and permutation entropy (PE) algorithm. Firstly, an analysis method combining Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to separate the original water level data into different frequency modal components. The PE algorithm subsequently divides each modal component into sequences of high and low frequencies. The GRU model is applied to predict the high-frequency sequence part, while the TCN–Transformer combination model is used for the low-frequency sequence part. The forecasting from both models are combined to obtain the final water level forecasting value. Multiple evaluation metrics are used to assess the forecasting performance. The findings indicate that the combined GRU–TCN–Transformer model achieves a Mean Absolute Error (MAE) of 0.0154, a Root Mean Square Error (RMSE) of 0.0205, and a Coefficient of Determination (R2) of 0.8076. These metrics indicate that the model outperforms machine learning Support Vector Machine (SVM) models, GRU models, Transformer models, and TCN–Transformer combination models in forecasting performance. The forecasting results have high credibility. This model provides a new reference for improving the accuracy of gate-front water level forecasting and offers significant insights for water resource management and flood prevention, demonstrating promising application prospects.

Microvascular destabilization is the primary cause of the inner blood-retinal barrier (iBRB) breakdown and increased vascular leakage in diabetic retinopathy (DR). Microvascular destabilization results from the combinational effects of increased levels of growth factors and cytokines, involvement of inflammation, and the changed cell-to-cell interactions, especially the loss of endothelial cells and pericytes, due to hyperglycemia and hypoxia. As the manifestation of microvascular destabilization, the fluid transports via paracellular and transcellular routes increase due to the disruption of endothelial intercellular junctional complexes and/or the altered caveolar transcellular transport across the retinal vascular endothelium. With diabetes progression, the functional and the structural changes of the iBRB components, including the cellular and noncellular components, further facilitate and aggravate microvascular destabilization, resulting in macular edema, the neuroretinal damage and the dysfunction of retinal inner neurovascular unit (iNVU). Although there have been considerable recent advances towards a better understanding of the complex cellular and molecular network underlying the microvascular destabilization, some still remain to be fully elucidated. Recent data indicate that targeting the intricate signaling pathways may allow to against the microvascular destabilization. Therefore, efforts have been made to better clarify the cellular and molecular mechanisms that are involved in the microvascular destabilization in DR. In this review, we discuss: (1) the brief introduction of DR and microvascular destabilization; (2) the cellular and molecular components of iBRB and iNVU, and the breakdown of iBRB; (3) the matrix and cell-to-cell contacts to maintain microvascular stabilization, including the endothelial glycocalyx, basement membrane, and various cell–cell interactions; (4) the molecular mechanisms mediated cell–cell contacts and vascular cell death; (5) the altered cytokines and signaling pathways as well as the intricate network of the cytokines involved in microvascular destabilization. This comprehensive review aimed to provide the insights for microvascular destabilization by targeting the key molecules or specific iBRB cells, thus restoring the function and structure of iBRB and iNVU, to treat DR.

The accurate forecasting of precipitation in the upper reaches of the Yellow River is imperative for enhancing water resources in both the local and broader Yellow River basin in the present and future. While many models exist for predicting precipitation by analyzing historical data, few consider the impact of different frequency sequences on model accuracy. In this study, we propose a coupled monthly precipitation prediction model that leverages the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit neural network (GRU), and attention mechanism-based transformer model. The permutation entropy (PE) algorithm is employed to partition the data processed by CEEMDAN into different frequencies, with different models utilized to predict different frequencies. The predicted results are subsequently combined to obtain the monthly precipitation prediction value. The model is applied to precipitation prediction in four regions in the upper reaches of the Yellow River and compared with other models. Evaluation results demonstrate that the CEEMDAN-GRU-Transformer model outperforms other models in predicting precipitation for these regions, with a coefficient of determination R2 greater than 0.8. These findings suggest that the proposed model provides a novel and effective method for improving the accuracy of regional medium and long-term precipitation prediction.

Understanding the causes and factors related to readmission for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) within a nationwide database including all payers and ages can provide valuable input for the development of generalizable readmission reduction strategies. To determine the rates, causes, and predictors for early (3-, 7-, and 30-d) readmission in patients hospitalized with AECOPD in the United States using the Nationwide Readmission Database after the initiation of the Hospital Readmissions Reduction Program, but before its expansion to COPD. We conducted an analysis of the Nationwide Readmission Database from 2013 to 2014. Index admissions and readmissions for an AECOPD were defined consistent with Hospital Readmissions Reduction Program guidelines. We investigated the percentage of 30-day readmissions occurring each day after discharge and the most common readmission diagnoses at different time periods after hospitalization. The relationship between predictors (categorized as patient, clinical, and hospital factors) and early readmission were evaluated using a hierarchical two-level logistic model. To examine covariate effects on early-day readmission, predictors for 3-, 7-, and 30-day readmissions were modeled separately. There were 202,300 30-day readmissions after 1,055,830 index AECOPD admissions, a rate of 19.2%. The highest readmission rates (4.2-5.5%) were within the first 72 hours of discharge, and 58% of readmissions were within the first 15 days. Respiratory-based diseases were the most common reasons for readmission (52.4%), and COPD was the most common diagnosis (28.4%). Readmission diagnoses were similar at different time periods after discharge. Early readmission was associated with patient (Medicaid payer status, lower household income, and higher comorbidity burden) and clinical factors (longer length of stay and discharge to a skilled nursing facility). Predictors did not vary substantially by time of readmission after discharge within the 30-day window. Thirty-day readmissions after an AECOPD remain a major healthcare burden, and are characterized by a similar spectrum of readmission diagnoses. Predictors associated with readmission include both patient and clinical factors. Development of a COPD-specific risk stratification algorithm based on these factors may be necessary to better predict patients with AECOPD at high risk of early readmission.

Lung adenocarcinoma (LUAD) is a common and deadly subtype of lung cancer with high mortality and limited treatment options. Enhancer RNAs (eRNAs) have emerged as important regulators in cancer biology, but their specific roles in LUAD have not been fully explored. This study aimed to investigate the role of eRNAs in LUAD pathogenesis and evaluate their potential as diagnostic biomarkers and therapeutic targets. Through integrated bioinformatics and experimental approaches, we identified differentially expressed eRNAs associated with LUAD progression. Functional analysis demonstrated that these eRNAs regulate critical pathways related to cell growth, division, repair, immune response, and tumor development. We constructed eRNA-centric regulatory networks, elucidating their interactions with transcription factors, enhancer-promoter loops, and RNA-binding proteins. Notably, eRNA ENSR00000188682 was highlighted for its central role, along with its associated transcription factor and downstream target gene ADRB2. Additionally, we developed a diagnostic prediction model based on eRNA expression profiles, which showed promising diagnostic accuracy. Our findings provide a comprehensive understanding of eRNA-mediated regulation in LUAD and suggest that eRNAs hold significant potential as prognostic biomarkers and therapeutic targets for improved clinical outcomes.

IntroductionThe role of the host immune response could be critical in the development of Treponema pallidum (Tp) infection in individuals with latent syphilis. This study aims to investigate the alterations in T follicular helper T (Tfh) cell balance among patients with secondary syphilis and latent syphilis.Methods30 healthy controls (HCs), 24 secondary syphilis patients and 41 latent syphilis patients were enrolled. The percentages of total Tfh, ICOS+ Tfh, PD-1+ Tfh, resting Tfh, effector Tfh, naïve Tfh, effector memory Tfh, central memory Tfh,Tfh1, Tfh2, and Tfh17 cells in the peripheral blood were all determined by flow cytometry.ResultsThe percentage of total Tfh cells was significantly higher in secondary syphilis patients compared to HCs across various subsets, including ICOS+ Tfh, PD-1+ Tfh, resting Tfh, effector Tfh, naïve Tfh, effector memory Tfh, central memory Tfh, Tfh1, Tfh2, and Tfh17 cells. However, only the percentages of ICOS+ Tfh and effector memory Tfh cells showed significant increases in secondary syphilis patients and decreases in latent syphilis patients. Furthermore, the PD-1+ Tfh cells, central memory Tfh cells, and Tfh2 cells showed significant increases in latent syphilis patients, whereas naïve Tfh cells and Tfh1 cells exhibited significant decreases in secondary syphilis patients when compared to the HCs. However, no significant change was found in resting Tfh and effector Tfh in HCs and secondary syphilis patients or latent syphilis patients.DiscussionDysregulated ICOS+ Tfh or effector memory Tfh cells may play an important role in immune evasion in latent syphilis patients.

The Qin-Mang River Basin is an important biodiversity conservation area in the Yellow River Basin. Studying the spatiotemporal changes in its ecosystem services (ESs) and the trade-offs and synergies (TOSs) between them is crucial for regional ecological protection and high-quality development. This study, based on land use type (LUT), and meteorological and soil data from 1992 to 2022, combined with the InVEST model, correlation analysis, and spatial autocorrelation analysis, explores the impacts of land use/land cover changes (LUCCs) on ESs. The results show that: (1) driven by urbanization and economic development, the expansion of built-up areas has replaced cultivated land and forests, with 35,000 hectares of farmland lost, thereby increasing pressure on ESs; (2) ESs show an overall downward trend, habitat quality (HQ) has deteriorated, carbon storage (CS) remains stable but the area of low CS has expanded, and sediment delivery ratio (SDR) and water yield (WY) fluctuate due to human activities and climate influence; (3) the TOSs of ESs change dynamically, with strong synergies among HQ, CS, and SDR. However, in areas with water scarcity, the negative correlation between HQ and WY has strengthened; (4) spatial autocorrelation analysis reveals that in 1992, significant positive synergies existed between ESs in the northern and northwestern regions, with WY negatively correlated with other services. By 2022, accelerated urbanization has intensified trade-off effects in the southern and eastern regions, leading to significant ecological degradation. This study provides scientific support for the sustainable management and policymaking of watershed ecosystems.

Habitat quality (HQ) is a critical factor for regional ecosystem health and sustainable development, as well as an important basis for formulating ecological protection and land-use planning. The Qin-Mang River Basin, as an integral part of the biodiversity conservation area in the Yellow River Basin, plays a significant role in maintaining the balance and stability of the regional ecosystem. This study is based on land use/land cover changes (LUCC) data from 1992, 2002, 2012, and 2022. It employs a land use transfer matrix to analyze the dynamic trends and patterns of LUCC. HQ changes are evaluated using the InVEST model, and the GeoDetector model is used to identify the key driving factors and their interactions. Additionally, spatial autocorrelation analysis is applied to explore the spatial clustering characteristics of HQ. The results indicate that between 1992 and 2022, the cumulative area of land transfer in the study area exceeded 600 km 2 , primarily characterized by the conversion of cultivated land to built-up areas. The HQ index decreased from 0.3409 in 1992 to 0.2896 in 2022, with a significant increase in spatial heterogeneity. Altitude, vegetation coverage, temperature, precipitation, and slope are the main driving factors influencing HQ, with natural factors dominating, but human activities gradually playing an increasingly significant role. Furthermore, HQ exhibits significant spatial clustering characteristics, with hotspot and coldspot areas providing scientific evidence for ecological protection and restoration measures. To improve HQ, it is recommended to strictly enforce ecological protection red lines, control the expansion of built-up areas, improve ecological compensation mechanisms, and promote ecological restoration measures such as returning farmland to forest and grassland.