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The general consensus in the field of Buddhist studies is that the terms “biaoquan” and “zhequan” are a pair of Buddhist philosophical concepts often used to designate two diametrically opposed forms of rhetoric. The former term constitutes its affirmative statement, while the latter defines a fact in negative terms—known in Christian theology as cataphatic and apophatic uses of language, respectively. Looking at the terms for which biaoquan and zhequan initially served as translations, especially in Xuanzang’s works, it would seem that these two concepts have not always appeared as a related pair representing the above-mentioned affirmative–negative dichotomy. The former could designate both affirmation (*vidhi) as well as the general activity of speech, syllables, and words (nāma). In the case of zhequan, it corresponds, in different texts, to the three Indian Buddhist concepts of negation (*pratiṣedha, *vyāvṛtti, *nivṛtti), implicative negation (paryudāsa), and exclusion of others (anyāpoha), with each use of the term “zhequan” carrying a different set of meanings and associated doctrines. Indeed, in various texts, the concept of zhequan might be opposed to the concept of biaoquan (*vidhi *sadhana) or opposed to pure negation (prasajya), or it might be applied on its own with no opposing concept. However, as Chinese Buddhism continued to develop throughout the Tang, biaoquan and zhequan came to be firmly associated and popularized as a pair of opposites. Looking at the doctrinal as well as the translation history of these two terms, this paper focuses on how they were used as a pair of opposing philosophical concepts, followed by an analysis of the profound influence of these two concepts on Chinese Buddhism.

As a key tool for real-time monitoring of air pollutant concentrations, the chemical sensor, the core component of the low-cost Air Quality Monitor (AQM), is susceptible to a variety of factors during the measurement process, leading to errors in the measurement data. To enhance the measurement accuracy of chemical sensors, this paper presents a calibration method based on the PCR-GPR model. This method not only effectively enhances the measurement accuracy of chemical sensors, but also combines the interpretability of traditional statistical models with the high-precision characteristics of Gaussian Process Regression (GPR) models. First, we perform Principal Component Analysis (PCA) on the measurement data of the AQM to solve the multicollinearity problem. Through PCA, we successfully extracted 8 principal components, which not only contained 95% of the information in the original data, but also effectively eliminated the correlation between the variables, providing a more robust data base for subsequent modeling. Subsequently, we established a Principal Component Regression (PCR) model using the concentration of pollutants measured by the national monitoring station as the dependent variable and the 8 principal components extracted above as the independent variables. The PCR model can effectively extract the linear relationship between the independent and dependent variables, providing a linear part of the explanation for the calibration process. However, there are often complex nonlinear relationships between pollutant concentrations and AQM measurements. To capture these nonlinear relationships, we further established a GPR model with the residuals of the PCR model as the dependent variable and the measurement data of the AQM as the independent variable. By combining the PCR model and the GPR model, we obtained the final PCR-GPR calibration model. It is worth mentioning that this study adopted the time series cross-validation method for data grouping, an innovative approach that is more aligned with real-world scenarios and adequately captures the seasonal variations in pollutant concentrations. The experimental results show that the model exhibits excellent performance on several evaluation metrics and can calibrate the chemical sensor well, improving its measurement accuracy by 16.94% ~ 82.01%.

Acid mine drainage (AMD) contains toxic yet valuable heavy metals. Conventional chemical neutralization using hydrated lime is highly efficient in mitigating heavy metal pollution, yet it generates hazardous solid wastes and is inefficient in recovering critical metal resources. Here, we report the design and demonstration of an electrochemically activated limestone (EAL) system for AMD treatment and valuable metal extraction. The EAL system extracted 82.3-100.0% of different metals (Cu, Cd, Zn) with high purity (~81.3 wt% valuable metals) through electro-reduction and local high pH-mediated precipitation when treating simulated AMD with varied conditions and actual AMD at energy consumption of 0.006-0.180 kW·h g −1 metal. In addition, the effluent after the EAL treatment contains high alkalinity and Ca 2+ , which is proven to have CO 2 sequestration potential. Overall, the EAL treatment reduces total costs by 17.9% and CO 2 emissions by 60.1% compared to conventional lime dosing treatment, demonstrating its economic viability and ecological superiority for the practical remediation of AMD and extraction of valuable metals. Acid mine drainage (AMD) contains toxic metals that harm ecosystems. Here, Lei et al. developed an electrochemically activated limestone system to treat AMD, extracting 82.3-100% of metals with high purity, reducing costs by 17.9%, and CO 2 emissions by 60.1%.

Catalytic biodiesel production with bases can be achieved under relatively mild conditions. However, the basicity of solid alkali catalysts originates usually from electron-rich atoms such as oxygen and nitrogen, rather than electron-deficient metal species. This typically induces aggregation and leaching of active sites, and difficulty in recycling. Here we synthesized a photothermal catalyst made of stable and uniformly dispersed graphene-like biomaterial anchored neighboring potassium single atoms. The production of biodiesel from various acidic oils over this catalyst was evaluated by life cycle assessment and cost analysis. Infrared thermal imaging and finite element simulations were used to study the light-induced self-heating process. We further studied the alkaline behavior of neighboring potassium single atoms by carbon dioxide chemisorption and quantum calculations. Results show biodiesel yield of 99.6% at room temperature, which is explained by a good local photothermal effect at the solar interface and the presence of superalkali sites in the atomic potassium-containing biomaterial. The global warming potential measured for this system resulted in a net negative CO2 emission of −10.8 kg CO2eq/kg. The photothermal catalyst can be recycled with almost no decline in reactivity.

Light source has obvious absorption and scattering effects during the transmission process of biological tissues, making it difficult to identify heterogeneities in multi-spectral images. This paper achieves a gradual improvement in the classification accuracy of heterogeneities on multi-spectral transmission images (MTI) through the combination of modulation-demodulation frame accumulation (M_D-FA) techniques and enhanced Visual Geometry Group 16 (VGG16) models. Firstly, experiments are designed to collect MTI of phantoms. Then, the image is preprocessed by different combinations of frame accumulation (FA) and modulation and demodulation (M_D) techniques. Finally, multi-spectral fusion pseudo-color images obtained from U-Net semantic segmentation are inputted into the original and enhanced VGG16 network models for heterogeneous classification. The experimental results show that: While both FA and M_D significantly improved the image quality individually, their combination (M_D-FA) proved superior, yielding the highest signal-to-noise ratio (SNR) and the most accurate heterogeneous classification. Compared to the original VGG16 model, the enhanced VGG16 models gradually improved the classification accuracy. Most importantly, the 3.5 Hz M_D-FA images processed by the Visual Geometry Group 16-Batch Normalization-Squeeze and Excitation-Global Average Pooling (VGG16_BN_SE_GAP) model achieved the highest classification accuracy of 97.57%, significantly outperforming results using FA or M_D alone. In summary, this paper utilizes different combinations of FA and M_D techniques to further improve the accuracy of deep learning networks on multi-spectral images heterogeneous classification, which promotes the clinical application of multi-spectral transmission imaging technology in early breast cancer detection.

Phosphofructokinase-1 (PFK-1), the core rate-limiting enzyme of glycolysis, has transcended its classical metabolic regulatory role and emerged as a multi-dimensional hub in tumour biology. This review systematically delineates the dynamic regulatory networks of PFK-1 isoforms (PFKP, PFKL, PFKM) in cancer: epigenetic remodelling drives tissue-independent expression reprogramming; post-translational modification networks confer metabolic–signalling dual functions; and the dynamic nature of its subcellular localization facilitates noncanonical roles, such as intranuclear transcriptional regulation. These mechanisms collectively orchestrate hallmark oncogenic processes, including tumour proliferation, metastatic invasion, cell death evasion, angiogenesis, immune escape, and metabolic reprogramming. In clinical translation, PFK-1 isoform expression profiles, modification states, and subcellular dynamics exhibit robust correlations with cancer diagnosis, prognosis, and therapeutic response. The isoform-specific modification networks unveil novel targets for developing diagnostic biomarkers and tissue-selective therapeutic strategies. This work not only reestablishes the central role of PFK-1 in tumour metabolic plasticity but also offers a fresh perspective for overcoming cancer treatment challenges. Graphical abstract

Dry eye can damage the ocular surface and result in mild corneal epithelial defect to blinding corneal pannus formation and squamous metaplasia. Significant progress in the treatment of dry eye has been made in the last two decades; progressing from lubricating and hydrating the ocular surface with artificial tear to stimulating tear secretion; anti-inflammation and immune regulation. With the increase in knowledge regarding the pathophysiology of dry eye, we propose in this review the concept of ocular surface microenvironment. Various components of the microenvironment contribute to the homeostasis of ocular surface. Compromise in one or more components can result in homeostasis disruption of ocular surface leading to dry eye disease. Complete evaluation of the microenvironment component changes in dry eye patients will not only lead to appropriate diagnosis, but also guide in timely and effective clinical management. Successful treatment of dry eye should be aimed to restore the homeostasis of the ocular surface microenvironment.

Metabolic reprogramming has been defined as a hallmark of malignancies. Prior studies have focused on the single nucleotide polymorphism (SNP) of POLG2 gene, which is reportedly responsible for encoding mitochondrial DNA genes and is implicated in the material and energy metabolism of tumor cells, whereas its function in prostate cancer has been elusive. Gene expression profile matrix and clinical information were downloaded from TCGA (The Cancer Genome Atlas) data portal, and GSE3325 and GSE8511 were retrieved from GEO (Gene Expression Omnibus) database. We conducted analysis of the relative expression of POLG2, clinical characterization, survival analysis, GO / KEGG and GSEA (Gene Set Enrichment Analysis) enrichment analysis in R and employed STRING portal to acquaint ourselves with the protein-protein interaction (PPI). IHC (Immunohistochemical) profiles of POLG2 protein between normal and cancerous tissues were consulted via HPA (Human protein atlas) database and the immunohistochemical POLG2 were verified between para-cancerous and cancerous tissues in tissue array. At the cellular level, Mitochondrial dysfunction assay, DNA synthesis test, wound healing assay, and invasion assay were implemented to further validate the phenotype of POLG2 knockdown in PCa cell lines. RT-qPCR and western blotting were routinely adopted to verify variations of molecular expression within epithelial mesenchymal transition (EMT). Results showed that POLG2 was over-expressed in most cancer types, and the over-expression of POLG2 was correlated with PCa progression and suggested poor OS (Overall Survival) and PFI (Progress Free Interval). Multivariate analysis showed that POLG2 might be an independent prognostic factor of prostate cancer. We also performed GO/KEGG, GSEA analysis, co-expression genes, and PPI, and observed the metabolism-related gene alterations in PCa. Furthermore, we verified that POLG2 knockdown had an inhibitory effect on mitochondrial function, proliferation, cell motility, and invasion, we affirmed POLG2 could affect the prognosis of advanced prostate cancer via EMT. In summary, our findings indicate that over-expressed POLG2 renders poor prognosis in advanced prostate cancer. This disadvantageous factor can serve as a potential indicator, making it possible to target mitochondrial metabolism to treat advanced prostate cancer.

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

The human microbiome plays a key role in drug development and precision medicine, but understanding its complex interactions with drugs remains a challenge. Identifying microbe-drug associations not only enhances our understanding of their mechanisms but also aids in drug discovery and repurposing. Traditional experiments are expensive and time-consuming, making computational methods for predicting microbe-drug associations a new trend. Currently, computational methods specifically designed for this task are still scarce. Therefore, to address the shortcomings of traditional experimental methods in predicting potential microbe-drug associations, this paper proposes a new prediction model named GCNATMDA. The model combines two deep learning models, Graph Convolutional Network and Graph Attention Network, and aims to reveal potential relationships between microbes and drugs by learning related features. Thus improve the efficiency and accuracy of prediction. We first integrated the microbe-drug association matrix from the existing dataset, and then combined the calculated microbe-drug characteristic matrix as the model input. The GCN module is used to dig deeper into the potential characterization of microbes and drugs, while the GAT module further learns the more complex interactions between them and generates the corresponding score matrix. The experimental results show that the GCNATMDA model achieves 96.59% and 93.01% in AUC and AUPR evaluation indexes, respectively, which is significantly better than the existing prediction models. In addition, the reliability of the prediction results is verified by a series of experiments.