Exosomal miRNAs: Biological Properties and Therapeutic Potential

MicroRNAs (miRNAs), small non-coding regulatory RNAs that regulate gene expression at the post-transcriptional level, are master regulators of a wide array of cellular processes. Altered miRNA expression could be a determinant of disease development and/or progression and manipulation of miRNA expression represents a potential avenue of therapy. Exosomes are cell-derived extracellular vesicles that promote cell–cell communication and immunoregulatory functions. These “bioactive vesicles” shuttle various molecules, including miRNAs, to recipient cells. Inappropriate release of miRNAs from exosomes may cause significant alterations in biological pathways that affect disease development, supporting the concept that miRNA-containing exosomes could serve as targeted therapies for particular diseases. This review briefly summarizes recent advances in the biology, function, and therapeutic potential of exosomal miRNAs

Representation Class and Geometrical Invariants of Quantum States under Local Unitary Transformations

We investigate the equivalence of bipartite quantum mixed states under local unitary transformations by introducing representation classes from a geometrical approach. It is shown that two bipartite mixed states are equivalent under local unitary transformations if and only if they have the same representation class. Detailed examples are given on calculating representation classes.Comment: 11 page

Simulation analysis on inner flow field and optimization design of air knife

This paper conducted a parametric modeling for air knife structure in a printing factory, used HYPERMESH software to divide the meshes of air model and combined with actual conditions to define various boundary conditions in the inner flow field of air knife. Meanwhile, this paper adopted fluid dynamics software Fluent to conduct numerical simulation for the internal airflow of air knife, obtained the distribution regulation of flow field, conducted a parametric modeling for air knife structure under many internal structural proposals through ANSYS design module based on the simulation computational result, conducted optimization design for the position of guide plates, the number of outlets and the size of return air tank in the detailed structure in the air knife in order to determine specific dimension parameters and optimal proposals. Based on the computational results of simulation, this paper found that the original air knife structure had a non-uniform flow field and low velocity at the inlet and outlets. With the increase of length of air knife, the velocity of the middle outlet reduced to zero and did not have obvious effects any more. Guide plates in the air knife had a great influence on the inner flow field of air knife. Through optimization design, the inner flow field of air knife became uniform when there was only one guide plate. When the guide plate was close to the front end of the air knife, the inner flow field of air knife was relatively uniform and velocity at the inlet and outlets was relatively high. This paper conducted a model design for air knives with different structural types and determined proposal 4 as the optimal design through repeated analysis. The design method in this paper could provide guidance for studying and designing air knife structures in the aspect of technological approach and theory

Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation

In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate object and part segmentation as an optimization problem and build a hierarchical feature representation including pixel, part, and object-level embeddings to solve it in a bottom-up clustering manner. Pixels are grouped into several clusters where the part-level embeddings serve as cluster centers. Afterwards, object masks are obtained by compositing the part proposals. This bottom-up interaction is shown to be effective in integrating information from lower semantic levels to higher semantic levels. Based on that, our novel approach Compositor produces part and object segmentation masks simultaneously while improving the mask quality. Compositor achieves state-of-the-art performance on PartImageNet and Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and demonstrates better robustness against occlusion by around 4.4% and 7.1% on part and object respectively. Code will be available at https://github.com/TACJu/Compositor