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● Fortunella genus consists of two populations: cultivated kumquat and wild Hong Kong kumquat. ● Artificial selection might involve in the origin of cultivated Fortunella species. ● A hypothesis for the differentiation and speciation of Fortunella species is proposed. Kumquat (Fortunella spp.) is a fruit and ornamental crop worldwide due to the palatable taste and high ornamental value of its fruit. Although Fortunella is classified into the economically important true citrus fruit tree group together with Citrus and Poncirus, few studies have been focused on its evolutionary scenario. In this study, analysis of five chloroplast loci and 47 nuclear microsatellites (nSSR) loci from 38 kumquat and 10 citrus accessions revealed the independent phylogeny of Fortunella among citrus taxa, and that Fortunella mainly comprises two populations: CUL, cultivated Fortunella spp. (F. margarita, F. crassifolia and F. japonica); and HK, wild Hong Kong kumquat (Fortunella hindsii). Genomic analysis based on whole-genome SNPs indicated that the allele frequency of both pupations deviated from the neutral selection model, suggesting directional selection was a force driving their evolutions. CUL exhibited lower genomic diversity and higher linkage strength than HK, suggesting artificial selection involved in its origin. A high level of genetic differentiation (Fst = 0.364) was detected and obviously asynchronous demographic changes were observed between CUL and HK. Based on these results, a new hypothesis for the speciation of Fortunella is proposed.

This paper comprehensively explores the integration of machine learning (ML) with atmospheric pressure plasma, highlighting its transformative impact in areas, such as modeling, diagnostics, and applications. The paper delves into the application of neural networks and deep learning models in simulating complex plasma dynamics, enhancing prediction accuracy, and reducing computational demands. We also examine the application of ML in plasma diagnostics, including real‐time data analysis and process optimization, demonstrating advancements in monitoring and controlling plasma systems. The article discusses the challenges encountered in this integration process, such as data quality, computational resources, and model interpretability. Finally, we outline future development directions, emphasizing the potential of ML in revolutionizing plasma research, improving operational efficiency, and opening new avenues in plasma technology. [ABSTRACT FROM AUTHOR]

In recent years, the in situ resource utilization of CO 2 in the Martian atmosphere by low-temperature plasma technology has garnered significant attention. However, numerical simulation is extremely time-consuming for modeling the complex CO 2 plasma, involving tens of species and hundreds of reactions, especially under Martian pressure. In this study, a deep neural network (DNN) with multiple hidden layers is introduced to investigate the CO 2 conversion in radio-frequency (RF) discharges at a given power density under Martian pressure in almost real time. After training on the dataset obtained from the fluid model or experimental measurements, the DNN shows the ability to accurately and efficiently predict the various discharge characteristics and plasma chemistry of RF CO 2 discharge even in seconds. Compared with conventional fluid models, the computational efficiency of the DNN is improved by nearly 10 6 times; thus, a real-time calculation of RF CO 2 discharge can almost be achieved. The DNN can provide an enormous amount of data to enhance the simulation results due to the very high computational efficiency. The numerical data also suggest that the CO 2 conversion increases with driving frequency at a fixed power density. This study shows the ability of the DNN-based approach to investigate CO 2 conversion in RF discharges for various applications, providing a promising tool for the modeling of complex non-thermal plasmas. [ABSTRACT FROM AUTHOR]

Bioceramics provide promising tactics for wound healing, yet the on‐shelf products are still unmet, especially for skin appendages regeneration. Herein, the inorganic magnesium silicate sprays (MSS) as mineral factors to overcome the existing challenges is proposed. Results indicate MSS can be synthesized by a green method without organic solvents, templates, calcination, and harmful by‐products. With amorphous phase, nanoscale, and high specific surface area, MSS reveal an improved pH‐responsive degradability and excellent bioactivity for cell proliferation and migration. In rats, MSS display a dose‐dependent effect on accelerating burn‐wound repair via regulating the expressions of iNOS and IL‐10 to attenuate inflammation, elevating the expressions of CD31 and α‐SMA to improve vascularization, and boosting collagen deposition without ectopic calcification. The released Mg and Si ions synergistically potentiate the expressions of Gap43, Tubb3, and K19, suggesting the regeneration of peripheral nerves and hair follicles. These superior features are further verified by comparisons with commercial products, Dermlin® and 45S5 bioglass®, in both rats and minipigs. Motivated by these findings, three MSS‐based formulations of band‐aid patch, adhesive hydrogel, and antibacterial sprays are devised for specific scenarios. Taken together, this proof‐of concept study provides a promising bioactive mineral for future clinical skin wound repair and functional appendages regeneration. [ABSTRACT FROM AUTHOR]

In recent years, plasma biomedicine has developed into a new interdisciplinary subject. The interaction between plasma and organisms involves three aspects: "gas–liquid–living organisms." Molecular dynamics (MD) simulation can simulate the interaction process between plasma and biological tissue with high temporal and spatial resolution. This article reviews the research on plasma biomedicine based on MD simulation and systematically summarizes the progress of MD simulation research on the interaction between plasma and water layer, cell membrane, intracellular substances, extracellular environment, and viruses. These simulations offer a unique opportunity to gain insights into the microscopic mechanisms of these interactions. This article also further looks forward to the opportunities and challenges of MD simulation research. [ABSTRACT FROM AUTHOR]