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Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field.

Prefocusing of the cell mixture is necessary for achieving a high-efficiency and continuous dielectrophoretic (DEP) cell separation. However, prefocusing through sheath flow requires a complex and tedious peripheral system for multi-channel fluid control, hindering the integration of DEP separation systems with other microfluidic functionalities for comprehensive clinical and biological tasks. This paper presented a simplified sheathless cell separation approach that combines gravitational-sedimentation-based sheathless prefocusing and DEP separation methods. Through gravitational sedimentation in a tubing, which was inserted into the inlet of a microfluidic chip with an adjustable steering angle, the cells were focused into a stream at the upstream region of a microchannel prior to separation. Then, a DEP force was applied at the downstream region of the microchannel for the active separation of the cells. Through this combined strategy, the peripheral system for the sheath flow was no longer required, and thus the integration of cell separation system with additional microfluidic functionalities was facilitated. The proposed sheathless scheme focused the mixture of cells with different sizes and dielectric properties into a stream in a wide range of flow rates without changing the design of the microfluidic chip. The DEP method is a label-free approach that can continuously separate cells on the basis of the sizes or dielectric properties of the cells and thus capable of greatly flexible cell separation. The efficiency of the proposed approach was experimentally assessed according to its performance in the separation of human acute monocytic leukemia THP-1 cells from yeast cells with respect to different sizes and THP-1 cells from human acute myelomonocytic leukemia OCI-AML3 cells with respect to different dielectric properties. The experimental results revealed that the separation efficiency of the method can surpass 90% and thus effective in separating cells on the basis of either size or dielectric property.