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Impacts of urbanization on energy balance in a central Amazonia city
Charging stations demand forecasting using LSTM based hybrid transformer model
Regional differences, dynamic evolution, and driving factors of ecological resilience in China’s urban agglomerations
Dynamics and functional roles of fungal communities in Pseudostellaria heterophylla soil under continuous cropping
Risk factors for unnatural mortality in persons with serious mental illness
Vitellogenin plays a role in regulating honey bee swarming
A multivariate analysis of the influencing factors for job satisfaction and well-being of academics
Clinical application of 3D reconstruction and accurate volume measurement of white matter in patients with cognitive dysfunction
Privacy preserving blockchain integrated explainable artificial intelligence with two tier optimization for cyber threat detection and mitigation in the internet of things
Electric impedance tomography to monitor body positioning and chest physiotherapy in mechanically ventilated pediatric intensive care unit patients
Safety and efficacy of super-high pressure OPN balloon in patients with in-stent restenosis - an intra-coronary imaging-based observational study
Movements induced by optic flow in relation to HINE
A novel framework for COPD management in cyber-physical systems using machine learning
Driving forces in the assembly of lipid nanoparticles containing mRNA revealed by molecular dynamics simulations at acidic and physiological pH
Abstract This study utilized all-atom molecular dynamics (MD) simulations to investigate the interactions and driving forces involved in the formation of mRNA-containing lipid nanoparticles (LNPs) at acidic pH (4.5) and physiological pH. Under the acidic condition, the LNP comprises mRNA, positively charged ionizable lipid (SM-102P), 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol, 1,2-dimyristoyl-rac-glycero-3-methoxypolyethylene glycol-2000 (DMG-PEG2000), and citrate ions with a charge of − 1. At physiological pH, it includes mRNA, both positively and neutrally charged ionizable lipids (SM-102P and SM-102N, respectively), DSPC, cholesterol, DMG-PEG2000, and citrate ions at − 1 and − 3 charges. MD analyses suggest that electrostatic forces play a significant role in mRNA and SM-102P interactions, which are crucial for mRNA encapsulation. Moreover, van der Waals forces are vital in the interactions between lipids during LNP formation, where at physiological pH, the lower polarity of SM-102N leads to stronger lipid interactions. Differences in the protonation states of ionizable lipids affect the hydrophobic interactions between lipid components in the LNP. Meanwhile, MD simulations in which all ionizable lipids are neutrally charged result in the mRNA not being encapsulated. Our finding offers insight into the self-assembly process of LNP, highlighting the crucial influence of pH and ionic strength on the encapsulation of mRNA by LNP.
Impact of removal frequency on site-specific force profile and dimensional stability of clear aligners in relation to dental crowding
Design and performance assessment of custom static intermixers in extrusion 3D printing using machine learning–driven image analysis
Colorless and transparent polyimide nanocomposite films containing organically modified fillers comprising an organoclay/functionalized-graphene complex
Proximity to water shapes the distribution of natural elephant mortality in Hwange National Park, Zimbabwe
Abstract While elephant poaching has received considerable attention, natural mortality can at times surpass human-induced deaths, especially under environmental stress. Understanding the ecological drivers of natural elephant mortality is therefore crucial for informing reintroduction efforts and preventing mass die-offs. In this study, we investigated environmental predictors of natural elephant mortality in Hwange National Park, Zimbabwe, using mortality records from 2020 to 2022. We applied four machine learning species distribution models, Random Forest, Gradient Boosting, Maximum Entropy, and Extreme Gradient Boosting, along with their ensemble to model mortality hotspots. The ensemble model outperformed individual models, achieving a True Skill Statistic of 0.54 and a Receiver Operating Characteristic of 0.83. Among all predictors, distance to water sources was the most influential variable (accounting for > 55% of model importance), with most mortalities occurring within 6 km of water points. Other key predictors included climate water deficit, normalized difference vegetation index (NDVI), tree cover percentage, and elephant density (each contributing > 5%). In contrast, maximum temperature of the warmest month and elevation had minimal predictive power (< 4%). Our results provide actionable insights for conservation planning. Areas close to water sources, particularly during dry periods, should be prioritized for monitoring and veterinary intervention. Meanwhile, regions with historically low mortality prevalences may serve as safer sites for reintroduction. This spatially explicit framework can help reduce post-release losses and enhance the long-term success of elephant conservation initiatives, especially in the face of ongoing environmental change.