The essential role of climate policy uncertainty in carbon emissions: a fresh insight.

This study assesses the influence of climate policy uncertainty (CPU) on carbon emissions (CE) against the backdrop of economic policy uncertainty (EPU) in the US. The wavelet analysis provides a comprehensive understanding of correlations in the time and frequency domains. The results demonstrate a significant correlation between CPU and CE, which varies across different time periods and frequencies. In the time domain, the results indicate that the CPU and CE move together during certain subperiods. Moreover, there are observable comovements in the frequency domain, particularly in the short to medium range. However, the correlation becomes stronger in the short term when there is no EPU, suggesting a closer interaction between CPU and CE. Therefore, it is crucial for governments to prioritize improving the clarity, credibility, and consistency of climate policies. They should also consider potential economic shocks when designing these policies.

Feasibility of tongue image detection for coronary artery disease: based on deep learning.

Aim: Clarify the potential diagnostic value of tongue images for coronary artery disease (CAD), develop a CAD diagnostic model that enhances performance by incorporating tongue image inputs, and provide more reliable evidence for the clinical diagnosis of CAD, offering new biological characterization evidence. Methods: We recruited 684 patients from four hospitals in China for a cross-sectional study, collecting their baseline information and standardized tongue images to train and validate our CAD diagnostic algorithm. We used DeepLabV3 + for segmentation of the tongue body and employed Resnet-18, pretrained on ImageNet, to extract features from the tongue images. We applied DT (Decision Trees), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), and XGBoost models, developing CAD diagnostic models with inputs of risk factors alone and then with the additional inclusion of tongue image features. We compared the diagnostic performance of different algorithms using accuracy, precision, recall, F1-score, AUPR, and AUC. Results: We classified patients with CAD using tongue images and found that this classification criterion was effective (ACC = 0.670, AUC = 0.690, Recall = 0.666). After comparing algorithms such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and XGBoost, we ultimately chose XGBoost to develop the CAD diagnosis algorithm. The performance of the CAD diagnosis algorithm developed solely based on risk factors was ACC = 0.730, Precision = 0.811, AUC = 0.763. When tongue features were integrated, the performance of the CAD diagnosis algorithm improved to ACC = 0.760, Precision = 0.773, AUC = 0.786, Recall = 0.850, indicating an enhancement in performance. Conclusion: The use of tongue images in the diagnosis of CAD is feasible, and the inclusion of these features can enhance the performance of existing CAD diagnosis algorithms. We have customized this novel CAD diagnosis algorithm, which offers the advantages of being noninvasive, simple, and cost-effective. It is suitable for large-scale screening of CAD among hypertensive populations. Tongue image features may emerge as potential biomarkers and new risk indicators for CAD.

Advances in Research on Protein Arginine Methyltransferase 2: Functions and Diseases.

Protein arginine methylation stands as a prevalent post-translational modification process, exerting vital roles in cellular signal transduction, gene expression, and cell cycle regulation. Amidst the protein arginine methyltransferase (PRMT) family, PRMT2 stands as a less explored constituent. Nonetheless, its regulatory roles in transcriptional regulation, post-transcriptional modification, methylation activity regulation, immunoregulation, and developmental regulation have garnered attention. These capabilities enable PRMT2 to exert pivotal regulatory functions in certain malignancies, metabolic disorders, inflammatory diseases, and atherosclerosis. In this review, we highlight the structure and functions of PRMT2, emphasizing its association with diseases. We also discuss PRMT2 inhibitors and explore the potential for therapeutic targeting.

A cation-π interaction confined graphene oxide membrane for separation of light paraffins and olefins.

A supported graphene oxide membrane is endowed with selective function for olefins by a cation intercalation method. The metal-cation fixed GO membrane exhibits a high propane to propylene ideal selectivity of 1817 for single gas and a separation factor of 7.1 for binary mixtures with fast gas permeance in the order of 10-7 mol m-2 s-1 Pa-1 and reliable permeation stability.

High Proton-Conductive and Temperature-Tolerant PVC-P4VP Membranes towards Medium-Temperature Water Electrolysis.

Water electrolysis (WE) is a highly promising approach to producing clean hydrogen. Medium-temperature WE (100-350 °C) can improve the energy efficiency and utilize the low-grade water vapor. Therefore, a high-temperature proton-conductive membrane is desirable to realize the medium-temperature WE. Here, we present a polyvinyl chloride (PVC)-poly(4vinylpyridine) (P4VP) hybrid membrane by a simple cross-linking of PVC and P4VP. The pyridine groups of P4VP promote the loading rate of phosphoric acid, which delivers the proton conductivity of the PVC-P4VP membrane. The optimized PVC-P4VP membrane with a 1:2 content ratio offers the maximum proton conductivity of 4.3 × 10-2 S cm-1 at 180 °C and a reliable conductivity stability in 200 h at 160 °C. The PVC-P4VP membrane electrode is covered by an IrO2 anode, and a Pt/C cathode delivers not only the high water electrolytic reactivity at 100-180 °C but also the stable WE stability at 180 °C.