Search

Gross primary productivity (GPP) is an important indicator in research on carbon cycling in terrestrial ecosystems. High-accuracy GPP prediction is crucial for ecosystem health and climate change assessments. We developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. We evaluated four models' behavior—Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN—in predicting GPP at nine flux observation sites on the Tibetan Plateau. The GeoMAN model achieved the best results (R2 = 0.870, RMSE = 0.788 g Cm−2 d−1, MAE = 0.440 g Cm−2 d−1). Distance and vegetation type of the flux sites influenced GPP prediction, with the latter being more significant. The different grassland vegetation types exhibited different sensitivity to environmental factors (Ta, PAR, EVI, NDVI, and LSWI) for GPP prediction. Among them, the site located in the alpine swamp meadow was insensitive to changes in environmental factors; the GPP prediction accuracy of the site located in the alpine meadow steppe decreased significantly with the changes in environmental factors; and the GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes, but to a lesser extent than the former. This study provides a good reference that deep learning model is able to achieve good accuracy in GPP simulation when considers spatial, temporal, and environmental factors, and the judgement made by deep learning model conforms to basic knowledge in the relevant field. [ABSTRACT FROM AUTHOR]

As one of the most promising candidates for all-solid-state sodium-ion batteries and sodium-metal batteries, polyvinylidene difluoride (PVDF) and amorphous hexafluoropropylene (HFP) copolymerized polymer solid electrolytes still suffer from a relatively low room temperature ionic conductivity. To modify the properties of PVDF-HEP copolymer electrolytes, we introduce the graphitic C3N4 (g−C3N4) nanosheets as a novel nanofiller to form g−C3N4 composite solid polymer electrolytes (CSPEs). The analysis shows that the g−C3N4 filler can not only modify the structure in g−C3N4CSPEs by reducing the crystallinity, compared to the PVDF−HFP solid polymer electrolytes (SPEs), but also promote a further dissociation with the sodium salt through interaction between the surface atoms of the g−C3N4 and the sodium salt. As a result, enhanced electrical properties such as ionic conductivity, Na+ transference number, mechanical properties and thermal stability of the composite electrolyte can be observed. In particular, a low Na deposition/dissolution overpotential of about 100 mV at a current density of 1 mA cm−2 was found after 160 cycles with the incorporation of g−C3N4. By applying the g−C3N4 CSPEs in the sodium-metal battery with Na3V2(PO4)3 cathode, the coin cell battery exhibits a lower polarization voltage at 90 mV, and a stable reversible capacity of 93 mAh g−1 after 200 cycles at 1 C. [ABSTRACT FROM AUTHOR]

Background: Coronary artery distension and aneurysm are complications of Kawasaki disease in children. Purpose: To develop a Z-score regression model for coronary artery diameter in children that could be used as reference. Material and Methods: This retrospective analysis included children with normal heart structure between March 2013 and April 2017. Body surface area (BSA) was calculated. The diameters of the right coronary, left main coronary, left anterior descending, and circumflex arteries were measured by echocardiography. Pearson correlation analysis was used to establish linear, exponential, logarithmic, power, and square root regression models. Results: The analysis included 509 children (280 boys) aged 1 day to 15.2 years. Coronary artery diameters were significantly correlated with age, height, body mass, BSA, and BSA (r = 0.663–0.826; P < 0.05), with a stronger correlation for BSA than BSA (P < 0.05). The adjusted determination coefficients ( R a 2) were higher for the exponential and square root models than for the other models (P < 0.05). The random error term variance was constant for the exponential model (P > 0.05), and processing with the weighted least-square methods eliminated heteroscedasticity in the other models. The Z-scores were normally distributed for the exponential and square root models (P > 0.05). Conclusion: Overall, the square root model was the optimal equation for the calculation of coronary artery Z-score in Chinese Han children. This model could be used to facilitate the diagnosis of coronary artery distension in children with suspected Kawasaki disease. [ABSTRACT FROM AUTHOR]

As scientific research progresses, there is an increasing understanding of the importance of paternal epigenetics in influencing the health and developmental path of offspring. Prior to conception, the environmental exposures and lifestyle choices of fathers can significantly influence the epigenetic state of sperm, including DNA methylation and histone changes, among other factors. These alterations in epigenetic patterns have the potential for transgenerational transmission potential and may exert profound effects on the biological characteristics of descendants. Paternal epigenetic changes not only affect the regulation of gene expression patterns in offspring but also increase the risk to certain diseases. It is crucial to comprehend the conditions that fathers are exposed to before conception and the potential outcomes of these conditions. This understanding is essential for assessing personal reproductive decisions and anticipating health risks for future generations. This review article systematically summarizes and analyzes current research findings regarding how paternal pre-pregnancy exposures influence offspring as well as elucidates underlying mechanisms, aiming to provide a comprehensive perspective for an enhanced understanding of the impact that paternal factors have on offspring health. [ABSTRACT FROM AUTHOR]