Deep generative design of RNA aptamers using structural predictions.

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers.

OsCBL1 mediates rice response to local nitrate signaling: insights into regulatory networks and gene expression.

Nitrate is a significant source of nitrogen in soils and also serves as a critical signal for root development. Previous studies have demonstrated that the local nitrate supply promotes lateral root elongation primarily through local nitrate signals, rather than nutritional effects. In this study, we report that Calcineurin B-like protein 1 (OsCBL1) positively regulates local nitrate signaling, thereby triggering lateral root colonization, as revealed by a comparative analysis of the phenotype and whole transcriptome of the knockdown mutant (-KD) and the wild-type (WT).

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models.

Motivation: RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures.

Approach to nonparametric cooperative multiband segmentation with adaptive threshold.

We present a new nonparametric cooperative approach to multiband image segmentation. It is based on cooperation between region-growing segmentation and edge segmentation. This approach requires no input data other than the images to be processed.