A suberized exodermis is required for tomato drought tolerance.

Plant roots integrate environmental signals with development using exquisite spatiotemporal control. This is apparent in the deposition of suberin, an apoplastic diffusion barrier, which regulates flow of water, solutes and gases, and is environmentally plastic. Suberin is considered a hallmark of endodermal differentiation but is absent in the tomato endodermis. Instead, suberin is present in the exodermis, a cell type that is absent in the model organism Arabidopsis thaliana. Here we demonstrate that the suberin regulatory network has the same parts driving suberin production in the tomato exodermis and the Arabidopsis endodermis. Despite this co-option of network components, the network has undergone rewiring to drive distinct spatial expression and with distinct contributions of specific genes. Functional genetic analyses of the tomato MYB92 transcription factor and ASFT enzyme demonstrate the importance of exodermal suberin for a plant water-deficit response and that the exodermal barrier serves an equivalent function to that of the endodermis and can act in its place.

Predictive Value of Blood Markers in Pediatric Brain Tumors Using Machine Learning.

BACKGROUND: The brain tumor is the most common solid tumor in children. Blood markers in most malignancies are altered due to the effect of inflammatory mediators on the bone marrow. OBJECTIVE: This study aimed to predict the malignancy of pediatric brain tumors using blood markers. METHODS: The pediatric brain tumors were divided into benign and malignant groups. Blood markers, including RBC, WBC, neutrophil, lymphocyte, monocyte, platelet, neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio, and derived neutrophil to lymphocyte ratio were extracted. Differences in blood markers between two groups were assessed using statistical analysis. The accuracy of machine learning to determine pediatric brain tumors' malignancy was evaluated using blood markers and demographic information. RESULTS: Among 113 patients, 55 patients were in the benign tumor group, and 58 patients were in the malignant tumor group. In the statistical study of blood markers in two groups, LMR was significantly different and positively correlated with malignancy. Other blood markers were not significantly different between two groups. This study showed that support-vector machines using blood markers, age, and sex can differentiate benign and malignant pediatric brain tumors with 71.6% accuracy. CONCLUSIONS: Despite the statistically significant differences in blood markers in different grades of brain tumors in adults, their differences in pediatric brain tumors, except LMR, were not significant. Machine learning using blood markers can differentiate between benign and malignant pediatric brain tumors with 71.6% accuracy.