Exosomes are essential components produced by all cell types, originating from the endosomal pathway through the invagination of the cell membrane. Their unique physicochemical characteristics are crucial for various commercial applications. Typically, exosomes range in size from 50 to 200 nm. Exosomes derived from plant cells are larger than their animal cell counterparts and demonstrate a broader therapeutic potential. This review explores the promising research opportunities associated with plant-derived exosomes, summarizing studies on their biogenesis, characterization, isolation methods, and therapeutic applications. It also emphasizes the importance of targeted drug delivery and provides insights into engineering plant-derived exosomes with various drugs. Additionally, highlights of plant-derived exosomes as natural nano-inducers that facilitate inter-kingdom communication and cross-kingdom regulatory interactions are also elucidated herein. Henceforth, this study culminates in a multidimensional insight for innovative therapeutic strategies and biotechnological advancements in plant-derived exosome research.

Exosomes are produced by all cell types, their biogenesis is of endosomal origin, originating as a result of cell membrane invagination. Furthermore, physiochemical characteristics are important prerequisites for their commercial application. The representative size range of exosomes is 50-200 nm. Whereas, comparative to animal cell, exosomes from plant cell sources have a relatively larger size range, moreover, exhibit broader therapeutic potential. In this review study, we have briefly discussed the research potential of plant-derived exosomes. Including their earlier studies on biogenesis, characterization, isolation methods, and possible therapeutic applications. Additionally, we have quoted targeted drug delivery for targeted therapies and have provided insights of methods for customizing exosomes produced from plants with various drugs. Most-importantly, here we have embodied exosomes from plant sources are natural nano-inducers possessing the ability for inter-kingdom communication, resulting in cross-kingdom regulations.

Overlapping cells in plant suspension-culture microscopy pose a particular challenge, for instance, segmentation because a single pixel may belong to more than one cell. Most standard instance-segmentation methods are not designed for this setting and tend to treat overlapping objects as mutually exclusive regions. We instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding. This allows multiple predicted masks to occupy the same image region. We further introduce a mask-level multiplicity-consistency loss that encourages the predicted number of masks covering a pixel to agree with the underlying cell occupancy. We evaluate MC-SlotNet on a newly annotated dataset of 53 Siraitia grosvenorii suspension-culture micrographs containing 4131 full-cell instances acquired at 4×–40× magnification. Using grouped five-fold cross-validation and an overlap-preserving evaluation protocol, we compare the method with Mask R-CNN, SOLOv2, and Mask2Former. MC-SlotNet achieves the best performance on AP50 (0.800), mAP$50:95$ (0.565), F150 (0.842), all-ground-truth Dice (0.761), AJI+ (0.754), overlap-region Dice (0.705), and overlap-instance recall (0.828). Its AP75 (0.649) is comparable to Mask2Former’s (0.651). MC-SlotNet also has the lowest inference time among the evaluated methods, at 1.113 s/image. These results indicate that decoding full-cell masks independently, rather than enforcing an exclusive partition of image pixels, is well-suited to instance segmentation in plant suspension-culture microscopy images with substantial cell overlap.

Object-centric process mining addresses the limitations of traditional approaches, which often involve the lossy flattening of event data and obscure vital relationships among interacting objects. This paper presents a novel formal framework for Object-centric Event Data (OCED) that ensures the correctness of the meta-model and preserves native object-centric semantics prior to the system implementation. Our approach effectively leverages Alloy for precisely specifying temporal properties and structural relationships between objects and events. This guarantees thorough verification against predefined OCED constraints such as cross-object cardinality bounds and time-aware consistency rules, hence preventing common data integrity issues. We demonstrate the effectiveness of the proposed framework in discovering and validating implicit object dependencies in event logs, particularly when importing data into graph databases like Neo4j. This demonstrates how formal verification can avoid pitfalls that lead to data invisibility and improve knowledge graph creation, enrichment, and querying. To bridge theory and practice, our verified \emph{FOCED} is made accessible through automatically generated Python bindings, empowering industrial users without formal methods expertise. The code is available on GitHub \footnote{https://github.com/sabalati/FOCED}