イネ幼植物根のアンモニウム吸収調節における新規タンパク質リン酸化酵素ACTPK1の分子機能の解明

Tohoku University早川俊彦課

ABA signaling converts stem cell fate by substantiating a tradeoff between cell polarity, growth and cell cycle progression and abiotic stress responses in the moss Physcomitrium patens

Abscisic acid (ABA)-mediated abiotic stress tolerance causes plant growth inhibition. Under such stress conditions, some mosses generate de novo stress-resistant stem cells, also called brood cells or brachycytes, that do not exist under normal conditions. However, the cell physiological basis of the growth inhibition and the stem cell formation is not well understood. Here, we show that the ABA-induced growth inhibition of the moss Physcomitrium patens apical protonemal cells (protonemal stem cells) is mediated through a shift from asymmetric to symmetric cell division. This change of the cell division mode, and consequently change of stem cell activity, is substantiated by dampening cell polarity and cell proliferative activity through the altered distribution of cytoskeletal elements, the mitotic spindle and the vacuole, which results in the production of stress-resistant stem cells. Alteration of the cell physiological data is supported by the results of RNAseq analysis indicating rapid changes in both cell polarity and cell cycle regulation, while long-term treatments with ABA for 5 to 10 days impact mainly the transcriptional and translational regulation. The regulation of cell polarity and cell cycle genes suggests growth arrest mediated by small GTPases (ROPs) and their guanine exchange factors (ROPGEFs) and by cyclin and cyclin-dependent-kinase complex, respectively. Our data suggest that a tradeoff relationship between growth ability and abiotic stress response in the moss is substantiated by ABA signaling to suppress cell polarity and asymmetric cell growth and may play a pivotal role in stem cell fate conversion to newly produced stress-resistant stem cells

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page