Advances of machine learning-assisted small extracellular vesicles detection strategy.

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Timing of systemic resistance induced by local exogenous ABA application within clonal network of stoloniferous herb Centella asiatica subjected to low water availability.

Resistance traits of plants can be activated both at the damaged site and undamaged parts. Systemic resistance induced by local exogenous abscisic acid (ABA) application alleviated negative effect of low water availability on growth performance of clonal plant. However, timing of systemic resistance was poorly understood. Timing of systemic resistance refers to its activation and decay time within clonal network. Clonal fragment of Centella asiatica with four successive ramets (including first-oldest, second-older, third-old and fourth-young ramets) subjected to low water availability (20% soil moisture content) was used to explore effects of local exogenous ABA application on the timing of resistance activation and decay. Systemic resistance activated by local exogenous ABA application after 4 days remained at least 28 days. Compared with control, biomass accumulation of whole clonal fragment, root biomass and ratio of belowground to aboveground biomass significantly increased by local exogenous ABA application after 28 days. It is suggested that rapid activation and delay of resistance response induced by local exogenous ABA application within clonal network may improve fitness of clonal plant subjected to abiotic stress.

Osmotic Adjustment and Antioxidant System Regulated by Nitrogen Deposition Improve Photosynthetic and Growth Performance and Alleviate Oxidative Damage in Dwarf Bamboo Under Drought Stress.

Dwarf bamboo (Fargesia denudata) is a staple food for the endangered giant pandas and plays a critical role in the sub-alpine ecosystem. Characterized by shallow roots and expeditious growth, it is exceedingly susceptible to drought stress and nitrogen (N) deposition in the context of a changing global environment. However, a comprehensive picture about the interactive response mechanism of dwarf bamboo to the two factors, water regime and N deposition, is far from being given. Therefore, a completely randomized design with two factors of water regimes (well-watered and water-stressed) and N deposition levels (with and without N addition) of F. denudata was conducted. In view of the obtained results, drought stress had an adverse impact on F. denudata, showing that it destroyed ultrastructure integrity and induced oxidative damage and restricted water status in leaves and roots, as well as declined photosynthetic efficiency in leaves, especially in N non-deposition plants. Nevertheless, F. denudata significantly increased heat dissipation in leaves, regulated antioxidant enzymes activities, antioxidants contents, and osmoregulation substances concentrations in leaves and roots, as well as shifted biomass partitioning in response to drought stress. However, regardless of water availability, N deposition maintained better ultrastructure in leaves and roots, resulting in superior photosynthesis and growth of F. denudata. Additionally, although N deposition did not cause oxidative damage in well-watered plants, ameliorated the effects of drought stress on F. denudata through co-deploying heat dissipation in leaves, the antioxidant system in roots as well as osmotic adjustment in leaves and roots. Noticeably, the leaves and roots of F. denudata expressed quite distinct acclimation responses to drought resistance under N deposition.