Critical Features Research Articles

Omics in IgG4-related disease.

Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition - and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.

Adaptive Tabu Search Algorithm Based on Quantum Dwelling and KNN Classifier for Creative Game Selection in Preschool Education

In order to select the best creative games for preschool education more quickly and accurately, a method for selecting creative games for preschool education based on tabu search tabu search (TS) algorithm is proposed. The TS algorithm is improved by using quantum population, and an adaptive neighborhood mapping mechanism is formed to dynamically adjust the tabu length and accelerate the convergence of the algorithm. The KNN algorithm is used to calculate the initial solution of the characteristics of preschool creative games, and the quantum adaptive optimization tabu search algorithm is used to iterate from the initial solution, and the subset of feature vectors containing feature weights and feature selection vectors is continuously searched as the input of KNN classifier. The improved objective function is used as a guide to combine the selection results of classifiers by simple voting, and the output of the selection results of creative games in preschool education is realized. Experiments show that this method can select more critical features of creative games in preschool education at a faster speed while ensuring the accuracy of the selection results. At the same time, the improved tabu search algorithm has faster convergence speed and better search performance, and can recommend creative games that are most suitable for preschool education according to the actual situation.

Association between visceral lipid accumulation indicators and gallstones: a cross-sectional study based on NHANES 2017–2020

BackgroundObesity is a major contributing factor to the formation of gallstones. As early identification typically results in improved outcomes, we explored the relationship between visceral lipid accumulation indicators and the occurrence of gallstones.MethodsThis cross-sectional study involved 3,224 adults. The researchers employed multivariable logistic regression, smoothed curve fitting (SCF), threshold effects analysis, and subgroup analysis to examine the relationship between metabolic scores for visceral fat (METS-VF), waist circumference (WC), lipid accumulation products (LAP), and visceral adiposity index (VAI) and gallstones. A Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify key factors which were then used in the construction of a nomogram model. The diagnostic efficacy of this model in detecting gallstones was then determined using receiver operating characteristic curves.ResultsVisceral lipid accumulation indicators were strongly linked to the likelihood of having gallstones. Specific saturation effects for METS-VF, WC, LAP, and VAI and gallstones were determined using SCF. The inflection points for these effects were found to be 8.565, 108.400, 18.056, and 1.071, respectively. Subgroup analyses showed that associations remained consistent in most subgroups. The nomogram model, which was developed using critical features identified by LASSO regression, demonstrated excellent discriminatory ability, as indicated by an area under the curve value of 0.725.ConclusionsStudies have shown that increases in METS-VF, WC, LAP, and VAI are linked to increased prevalences of gallstones. The nomogram model, designed with critical parameters identified using LASSO regression, exhibits a strong association with the presence of gallstones.

Glutathione and Selenium Supplementation Attenuates Liver Injury in Diethylnitrosamine-Induced Hepatocarcinogenic Mice by Enhancing Glutathione-Related Antioxidant Capacities

Excess oxidative stress and inadequate antioxidant capacities are critical features in the development of hepatocellular carcinoma. This study aimed to determine whether supplementation with glutathione (GSH) and/or selenium (Se), as antioxidants, attenuates diethylnitrosamine (DEN)-induced hepatocarcinogenesis in mice. C57BL/6J male mice were randomly assigned to control, DEN, DEN + GSH, DEN + Se, and DEN + GSH + Se groups for 20 weeks. Daily supplementation with GSH and/or Se commenced in the first experimental week and continued throughout the study. DEN was administered in weeks 2–9 and 16–19 of the experimental period. DEN administration induced significant pathological alterations of hepatic foci, evidenced by elevated levels of liver function, accompanied by high malondialdehyde (MDA) levels; low GSH levels; and glutathione peroxidase (GPx), glutathione reductase (GR), and glutathione S-transferase (GST) activities. Supplementation with GSH and Se significantly ameliorated liver pathological changes, reducing liver function and MDA levels while increasing GSH levels and GPx, GR, and GST activities. Notably, combined supplementation with GSH and Se more effectively increased the GSH/glutathione disulfide ratio and GPx activity than individual supplementation. Supplementation with GSH and Se attenuated liver injury in DEN-induced hepatocarcinogenic mice by enhancing GSH and its related antioxidant capacities, thereby mitigating oxidative damage.

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R2 values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

Feature Vector Effectiveness Evaluation for Pattern Selection in Computational Lithography

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

Fracture toughness of alkali-activated concrete subjected to aggressive environment

This study investigates the fracture toughness of alkali-activated granulated blast-furnace slag (GBFS)-based geopolymer concrete (GPC), non-activated slag cement concrete, and ordinary Portland cement (OPC) concrete in acidic, alkaline, and chloride solutions over six months. A total of 580 centrally straight-notched Brazilian disc (CSNBD) specimens were cast to assess pure mode-I (0°), pure mode-II (28.7°), and mixed-mode (15° and 45°) fractures under these conditions. Microstructural analyses (SEM) were performed to evaluate microstructural changes after six months of exposure. Results showed that GPC exhibited the lowest initial fracture toughness but the smallest reduction (7 %-11 % depending on the mode) across all environments, with slag cement concrete and OPC experiencing reductions of 11 %-14 % and 14 %-21 %, respectively, in acidic conditions. In alkaline environments, GPC showed a 3 %-6 % decrease in fracture toughness, while OPC and slag cement concrete showed reductions of 6 %-13 %. The gradient boosting regressor accurately predicted the failure load of CSNBD specimens, achieving an R² score of 0.948. Finally, SHAP sensitivity analysis identified concrete type, exposure time, and environment as critical input features, consistent with experimental results.

Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States

Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought simulations performed by two widely used hydrological models. The evaluation approach captures how well a model classifies observed periods of drought and non-drought, quantifies error components during periods of drought, and assesses the models’ simulations of drought severity, duration, and intensity. We apply this approach at 4662 U.S. Geological Survey streamflow gages covering a wide range of hydrologic conditions across the conterminous U.S. from 1985 to 2016 to evaluate streamflow drought using two national-scale hydrologic models: the National Water Model (NWM) and the National Hydrologic Model (NHM); therefore, a benchmark against which to evaluate additional models is provided. Using this approach, we find that generally the NWM better simulates the timing of flows during drought, while the NHM better simulates the magnitude of flows during drought. Both models performed better in wetter eastern regions than in drier western regions. Finally, each model showed increased error when simulating the most severe drought events.

Motion-Adaptive Tessellated Skin Patches With Switchable Adhesion for Wearable Electronics.

Skin-interfaced electronics have emerged as a promising frontier in personalized healthcare. However, existing skin-interfaced patches often struggle to simultaneously achieve robust skin adhesion, adaptability to dynamic body motions, seamless integration of bulky devices, and on-demand, damage-free detachment. Here, a hybrid strategy that synergistically combines these critical features within a thin, flexible patch platform is introduced. This design leverages shape memory polymers (SMPs) arranged in a tessellated array, comprising both rigid and compliant SMPs. This configuration enables exceptional deformability, motion adaptability, and ultra-strong, repeatable skin adhesion while offering on-demand adhesion control. Furthermore, the design facilitates the seamless integration of bulky electronics without compromising skin adhesion. By incorporating sizeable electronics including signal acquisition circuits, sensors, and a battery, it is demonstrated that the proposed tessellated patch can be securely mounted on the skin, accommodate dynamic body motions, precisely detect physiological signals with an outstanding signal-to-noise ratio (SNR), wirelessly transmit data, and be effortlessly released from the skin.

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements.

Novel state of charge estimation method of containerized Lithium–Ion battery energy storage system based on deep learning

State of charge (SOC) is a critical indicator for lithium–ion battery energy storage system. However, model-driven SOC estimation is challenging due to the coupling of internal charging and discharging processes, ion diffusion, and chemical reactions in the electrode materials. These factors result in frequent changes in their internal characteristics. Therefore, we devise a novel deep learning SOC estimation method based on the attention mechanism (A) and convolutional neural networks-long short term memory (CNN–LSTM) network model. The CNN layers are employed to extract the critical features of the battery dataset. The LSTM layers are utilized to capture the long-term dependencies in the time series data. The attention mechanism can assign attention weights to different features within the output of the convolution layer. The A–CNN–LSTM method not only amplifies the positive influence of critical knowledge but also provides a global reference. The comparative experiments of three datasets and ablation study are carried out. The root mean square error, mean absolute error, and mean absolute percentage error obtained from the A–CNN–LSTM model decreased by 32.75 %, 45.88 %, and 53.36 %, respectively, compared with the best results obtained from the existing CNN, LSTM, gate recurrent unit (GRU), CNN–LSTM, and CNN–GRU models. The proposed model demonstrates greater stability and consistency than other models, underscoring its efficacy in SOC estimation.

A Novel Fault Diagnosis Method for Rolling Bearings Based on Multi-Domain Adaptation Neural Network

In recent years, data-driven approaches have shown promise in fault diagnosis. However, in practical industrial applications, challenges such as discrepancies between source domain and target domain data distributions, coupled with limited labeled fault data, have hindered the performance of traditional domain adaptation algorithms in bearing fault diagnosis. To address this issue, this study introduces a novel method based on MDANN (Multi Domain Adaptation Neural Network) for diagnosing rolling bearing faults using unlabeled data. Initially, the raw vibration signals are preprocessed using wavelet packet decomposition and reconstruction (WPT), which helps minimize signal redundancy while preserving critical features. Following this, the multi-kernel maximum mean discrepancy (MK-MMD) algorithm is employed to compute the differences in input feature values, and the MDANN network parameters are adjusted via backpropagation, allowing the network to extract domain-invariant features. To ensure the unlabeled target domain data can be effectively used in training, a pseudo-labeling strategy is applied, where the label with the highest probability is treated as the true label, thereby enabling the model to learn from the target domain data and improve the acquisition of reliable diagnostic knowledge. The method is validated using two publicly available datasets, CWRU and PU, with experimental results demonstrating that it outperforms conventional domain adaptation techniques in terms of diagnostic accuracy. This indicates the proposed approach is highly effective in capturing transferable features and addressing the distributional differences between datasets.

Rapid and high-accuracy concentration prediction of gas mixtures based on PMH-TCN

In industrial environments, the flammable gas ethylene (C2H4) and toxic gas carbon monoxide (CO) often coexist. To ensure worker safety, it is essential to predict these gas concentrations quickly and accurately. Many studies lack prediction accuracy or speed due to insufficient time feature extraction and loss of critical features. In this work, a novel approach called the Parametric Rectified Linear Unit (PReLU)-based Multi-Head Attention Temporal Convolutional Network (PMH-TCN) model was devised to efficiently and precisely predict gas concentrations in mixtures. The model is capable of accurately predicting the gas concentration within 5 s after the introduction of the target gas. In the 5-fold cross-validation, the values of Adjusted R-Square (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the PMH-TCN model reached 0.9850, 0.0448, and 0.0651 respectively. It was shown that the PMH-TCN model offered a very efficient method for the quick identification of electronic noses.

Framework and policy recommendations for the design of metaverse platforms in support of Saudi Vision 2030

Purpose This paper suggests a framework that describes the critical features and design elements that should be considered when creating a metaverse platform. This metaverse framework will assist in achieving the goals of Saudi Vision 2030, specifically in making the applied technology transformation goal a reality. In addition, this paper offers valuable recommendations and insights for policymakers to guarantee the actualization of the digital transformation associated with the metaverse in Saudi Vision 2030. Design/methodology/approach This research plans to address the gaps by conducting a thorough literature review to examine the characteristics and design elements of the latest metaverse platforms. It proposes a suggested framework that discusses the distinctive features that should be considered when creating any metaverse platform. In addition, the research provides policymakers with important recommendations and insights to ensure effective contribution to the digital transformation goals of Saudi Vision 2030, thereby supporting the scientific development of the metaverse platform Findings After a deep review of the literature, this paper proposes a conceptual framework as described in Figure 1, which identifies the most important design features and elements that need to be considered when building a metaverse platform, which will be a critical component of the digital infrastructure. These design elements include immersive engineering, accessibility, privacy, security, interoperability, digital assets and regulations. They are essential to developing a powerful metaverse platform that has all the features of this virtual reality environment. Immersive engineering concepts in the metaverse world aim to improve user experience by creating more realistic, interactive and engaging virtual environments. Research limitations/implications The findings of this study provide valuable insights into the development and implementation of metaverse platforms in Saudi Arabia by proposing a suggested framework that discusses the distinctive features that should be considered when creating metaverse platform and provides policymakers with some important recommendations and insights to ensure that effectively contributes to the realization of digital transformation, which is the one of Saudi Vision 2030. This paper carries several implications. First, this paper has an impact on future technology research, as it presented the most important design elements and advanced technologies for implementing metaverse platforms. In addition, it sheds the light on the limitations faced by Saudi Arabia metaverse platforms. Second, this research also has an impact on public policy in Saudi Arabia. This paper presented the most important recommendations that need to be taken into consideration when applying metaverse in Saudi Arabia [see section Recommendation]. Finally, it is seen that the presence of the metaverse not only facilitates but also supports digital transformation and the application of new technology. This research also has an indirect impact on society and the economy, as it is delineating the enhancements within these environments following the metaverse’s adoption, as elucidated in section Discussion. Social implications Metaverse platforms have societal impacts such as social engagement, entertainment, education, accessibility and cultural promotion. They can enhance inclusivity, improve well-being, showcase cultural heritage and boost tourism-related industries. Originality/value Proposed framework for metaverse platforms: The research proposes a suggested framework that outlines the distinctive features necessary for creating metaverse platforms. This framework is based on a thorough literature review and aims to guide the development of metaverse platforms that can support the objectives of Saudi Vision 2030. Policy recommendations: The study offers important recommendations and insights for policymakers. These recommendations are designed to ensure that policymakers can effectively contribute to the realization of the digital transformation goals of Saudi Vision 2030, particularly in the context of applied technology transformation.

Core-Periphery Multi-Modality Feature Alignment for Zero-Shot Medical Image Analysis.

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose structure is organized by the core-periphery (CP) principle. This auxiliary CP network not only aligns medical image and text features into a unified latent space more efficiently but also ensures alignment driven by principles of brain network organization. In this way, our approach effectively mitigates and further enhances CLIP's zero-shot performance in medical image analysis. More importantly, the proposed CP-CLIP exhibits excellent explanatory capability, enabling the automatic identification of critical disease-related regions in clinical analysis. Extensive experiments and evaluation across five public datasets covering different diseases underscore the superiority of our CP-CLIP in zero-shot medical image prediction and critical features detection, showing its promising utility in multimodal feature alignment in current medical applications.

Enhanced lithological mapping in arid crystalline regions using explainable AI and multi-spectral remote sensing data

Lithological classification is essential for understanding the spatial distribution of rocks, especially in arid crystalline areas. Artificial intelligence (AI) recent advancements with multi-spectral satellite imagery have been utilized to enhance lithological mapping in these areas. Here we employed different AI models namely, Support Vector Machine (SVM), Random Forest Classification (RFC), Logistic Regression, XGBoost, and K-nearest neighbors (KNN) for lithological mapping. This was followed by the application of explainable AI (XAI) for lithological discrimination (LD) which is still not widely explored. Based on the highest accuracy and F1 score of the previously mentioned models, RFC model outperformed all of them, and hence, it was integrated with XAI, using the SHapley Additive exPlanations (SHAP) method.This approach successfully identified critical multi-spectral features for LD in arid crystalline zones when applied on the Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and SRTM-DEM datasets covering the Hammash and the Wadi Fatimah areas in Egypt and the Kingdom of Saudi Arabia, respectively. Field validation in the Hammash area confirmed the RFC model's efficacy, achieving a satisfactory 94% overall accuracy for 18 features. SHAP was able to identify the top ten features for proper LD over the Hammash area with 90.3% accuracy despite the complex nature of the ophiolitic mélange. For validation purposes, RCF was then utilized in the Wadi Fatimah region, using only the top 10 critical features rendered from the SHAP analysis. It performed well and had 93% accuracy. Notably, XAI/SHAP results indicated that elevation data, Landsat-8's Green Band (B3), and the two ASTER SWIR bands (B5 and B6) were essential and significant for identifying island arc rocks. Moreover, the SHAP model effectively delineated complex mélange matrices, primarily using ASTER SWIR band (B8). Our findings highlight the successful combination of RFC with XAI for LD and its potential utilization in similar arid crystalline environments worldwide.

Schizophrenia pathology reverse-translated into mouse shows hippocampal hyperactivity, psychosis behaviors and hyper-synchronous events.

Decades of research into the function of the medial temporal lobe has driven curiosity around clinical outcomes associated with hippocampal dysfunction, including psychosis. Post-mortem analyses of brain tissue from human schizophrenia brain show decreased expression of the NMDAR subunit GluN1 confined to the dentate gyrus with evidence of downstream hippocampal hyperactivity in CA3 and CA1. Little is known about the mechanisms of the emergence of hippocampal hyperactivity as a putative psychosis biomarker. We have developed a reverse-translation mouse to study critical neural features. We had previously studied a dentate gyrus (DG)-specific GluN1 KO, which displays hippocampal hyperactivity and a psychosis-relevant behavioral phenotype. Here, we expressed an inhibitory DREADD (pAAV-CaMKIIa-hM4D(Gi)-mCherry) in granule cells of the mouse dentate gyrus, and continuously inhibited the region for 21 days in adolescent (6 weeks) and adult (10 weeks) C57BL/6 J mice with DREADD agonist Compound 21 (C21). Following this period, we quantified activity in the hippocampal subfields by assessing cFos expression, hippocampally mediated behaviors, and hippocampal local field potential with an intracerebral probe with continual monitoring over time. DG inhibition during adolescence generates an increase in hippocampal activity in CA3 and CA1, impairs social cognition and spatial working memory, as well as shows evidence of increased activity in local field potentials as spontaneous synchronous bursts of activity, which we term hyper-synchronous events (HSEs) in hippocampus. The same DG inhibition delivered during adulthood in the mouse lacks these outcomes. These results suggest a sensitive period in development in which the hippocampus is susceptible to DG inhibition resulting in hippocampal hyperactivity and psychosis-like behavioral outcomes.

From the Mandate of Heaven to the modern state: the nation according to Wei Xiaobao

Abstract This paper analyzes the Jin Yong novel The Deer and the Cauldron through the lens of Etienne Balibar's theory of super-nationalism and supranationalism. The novel employs a pan-Asian racial ideology to expand national identity from Han Chinese to other ethnic groups (supranationalism) by introducing a racial Other, white Europeans, to unify warring groups. Simultaneously, Han culture is consistently uplifted as superior (super-nationalism). A critical sequence features the Kangxi Emperor asserting his legitimacy as the ruler of China to the protagonist Wei Xiaobao by claiming the Mandate of Heaven has passed from the Ming to the Qing dynasty. However, Han Chinese gallants and intellectuals constantly challenge his legitimacy because, as a Manchu, he is considered foreign. To resolve this issue, Wei Xiaobao begins constructing a racial national framework that includes Manchus. This paper further argues that Wei Xiaobao's moral relativism, unusual for a protagonist in martial arts fiction, enables the flexibility to redefine Chinese identity on racial grounds instead of moral or cultural. The Deer and the Cauldron illustrates the transition from the Mandate of Heaven to modern nation-state ideology in China, in the form of an irreverent martial arts fiction novel, crafted by the genre's greatest master.

Identification of PANoptosis Subtypes to Assess the Prognosis and Immune Microenvironment of Lung Adenocarcinoma Patients: A Bioinformatics Combined Machine Learning Study.

PANoptosis, a novelty mechanism of cell death involving crosstalk between apoptosis, pyroptosis, and necroptosis, is strongly associated with tumor cell death and immunotherapy efficacy. However, its relevance in lung adenocarcinoma (LUAD) remains to be elucidated. In this study, we acquired 18 PANoptosis-related differentially expressed gene (PRDEG) of LUAD. Based on these genes, LUAD samples were identified with different sub-types by unsupervised clustering. Next, we compared the differences between the subtypes, including clinical features, immune microenvironment, and potentially sensitive drugs. Further-more, we used machine learning to identify hub prognostic PRDEGs, construct a risk score, and validate it on other external datasets. We incorporated the patient's clinical information and risk score into the proportional hazards model and lasso-cox models to find key prognostic features and constructed five prognostic models. The best model was identified via the area under the curve and validated on an external dataset. LUAD patients were divided into two clusters named C1 and C2, respectively. The C2 cluster exhibited shorter survival time, more advanced tumor stage, higher suppressive immune cell scores, such as dendritic cells, and higher expression of inhibitory immune checkpoints, such as LAG3 and CD86. TIMP1, CAV1, and CD69 were recognized as key prognostic factors, and risk scores predicted survival with significant differences in the external validation set. Risk score and N-stage were identified as critical prognostic features. The Coxph model outper-formed other machine learning clinical models. The 1-, 3-, and 5-year time-ROCs in the exter-nal validation set were 0.55, 0.59, and 0.60, respectively. We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of patients with LUAD as well as the tumor mi-croenvironment.