Inducible expression of Pisum sativum xyloglucan fucosyltransferase in the pea root cap meristem, and effects of antisense mRNA expression on root cap cell wall structural integrity

Mitosis and cell wall synthesis in the legume root cap meristem can be induced and synchronized by the nondestructive removal of border cells from the cap periphery. Newly synthesized cells can be examined microscopically as they differentiate progressively during cap development, and ultimately detach as a new population of border cells. This system was used to demonstrate that Pisum sativum L. fucosyl transferase (PsFut1) mRNA expression is strongly expressed in root meristematic tissues, and is induced >2-fold during a 5-h period when mitosis in the root cap meristem is increased. Expression of PsFut1 antisense mRNA in pea hairy roots under the control of the CaMV35S promoter, which exhibits meristem localized expression in pea root caps, resulted in a 50–60% reduction in meristem localized endogenous PsFut1 mRNA expression measured using whole mount in situ hybridization. Changes in gross levels of cell wall fucosylated xyloglucan were not detected, but altered surface localization patterns were detected using whole mount immunolocalization with CCRC-M1, an antibody that recognizes fucosylated xyloglucan. Emerging hairy roots expressing antisense PsFut1 mRNA appeared normal macroscopically but scanning electron microscopy of tissues with altered CCRC-M1 localization patterns revealed wrinkled, collapsed cell surfaces. As individual border cells separated from the cap periphery, cell death occurred in correlation with extrusion of cellular contents through breaks in the wall

3D image data of digitally-generated porous material samples

This dataset contains 3D image data of digitally-generated 528 granular porous material samples, which can be used to train a machine learning model for predicting material performance properties from geometric features of porous material samples. The dataset has been prepared in https://doi.org/10.1016/j.cej.2021.130069

Workflow for materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm

This dataset includes codes and data to describe the workflow of porous material structural optimization

Workflow for materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm

This dataset includes codes and data to describe the workflow of porous material structural optimization.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

3D image data of digitally-generated porous material samples

This dataset contains 3D image data of digitally-generated 528 granular porous material samples, which can be used to train a machine learning model for predicting material performance properties from geometric features of porous material samples. The dataset has been prepared in https://doi.org/10.1016/j.cej.2021.130069.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV