RsOBP2a, a member of OBF BINDING PROTEIN transcription factors, inhibits two chlorophyll degradation genes in green radish.

The green radish (Raphanus sativus L.) contains abundant chlorophyll (Chl). DOF-type transcription factor OBF BINDING PROTEIN (OBP) plays crucial functions in plant growth, development, maturation and responses to various abiotic stresses. However, the metabolism by which OBP transcription factors regulate light-induced Chl metabolism in green radish is not well understood. In this study, six OBP genes were identified from the radish genome, distributed unevenly across five chromosomes. Among these genes, RsOBP2a showed significantly higher expression in the green flesh compared to the white flesh of green radish. Analysis of promoter elements suggested that RsOBPs might be involved in stress responses, particularly in light-related processes. Overexpression of RsOBP2a led to increase Chl levels in cotyledons and adventitious roots of radish, while silencing RsOBP2a expression through TYMV-induced gene silencing accelerated leaf senescence. Further investigations revealed that RsOBP2a was localized in the nucleus and served as a transcriptional repressor. RsOBP2a was induced by light and directly suppressed the expression of STAYGREEN (SGR) and RED CHLOROPHYLL CATABOLITE REDUCTASE (RCCR), thereby delaying senescence in radish. Overall, a novel regulatory model involving RsOBP2a, RsSGR, and RsRCCR was proposed to govern Chl metabolism in response to light, offering insights for the enhancement of green radish germplasm.

Synergistic Machine Learning Accelerated Discovery of Nanoporous Inorganic Crystals as Non-Absorbable Oral Drugs.

Machine learning (ML) has taken drug discovery to new heights, where effective ML training requires vast quantities of high-quality experimental data as input. Non-absorbable oral drugs (NODs) have unique safety advantage for chronic diseases due to their zero systemic exposure, but their empirical discovery is still time-consuming and costly. Here, a synergistic ML method, integrating small data-driven multi-layer unsupervised learning, in silico quantum-mechanical computations, and minimal wet-lab experiments is devised to identify the finest NODs from massive inorganic materials to achieve multi-objective function (high selectivity, large capacity, and stability). Based on this method, a NH4-form nanoporous zeolite with merlinoite (MER) framework (NH4-MER) is discovered for the treatment of hyperkalemia. In three different animal models, NH4-MER shows a superior safety and efficacy profile in reducing blood K+ without Na+ release, which is an unmet clinical need in chronic kidney disease and Gordon's syndrome. This work provides a synergistic ML method to accelerate the discovery of NODs and other shape-selective materials.

A2OURSR: Adaptive adjustment based real MRI super-resolution via opinion-unaware measurements.

High-quality and high-resolution magnetic resonance (MR) images can provide more details for diagnosis and analyses. Recently, MR images guided neurosurgery has become an emerging technique in clinics. Unlike other medical imaging techniques, it is impossible to achieve both real-time imaging and high image quality in MR imaging. The real-time performance is closely related to the nuclear magnetic equipment itself as well as the collection strategy of the k space data. Optimizing the imaging time cost via the corresponding algorithm is harder than enhancing image quality. Further, in reconstructing low-resolution and noise-rich MR images, getting relatively high-definition and resolution MR images as references are difficult or impossible. In addition, the existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. As a result, severely bad results are inevitable when the modeling assumptions are far apart from the actual situation. To address these problems, we propose a novel adaptive adjustment method based on real MR images via opinion-unaware measurements for real super-resolution (A2OURSR). It can estimate the degree of blur and noise from the test image itself using two scores. These two scores can be considered pseudo labels to train the adaptive adjustable degradation estimation module. Then, the outputs of the above model are used as the inputs of the conditional network to tweak the generated results. Thus, the results can be automatically adjusted via the whole dynamic model. Extensive experimental results show that the proposed A2OURSR is superior to state-of-the-art methods on benchmarks quantitatively and visually.