On the change of growth and wood constructive substances in Salix Koriyanagi which was grown in different soil moisture conditions

textabstractThe cellular interactions that drive the formation and maintenance of the insulating myelin sheath around axons are only partially understood. Leucine-rich glioma-inactivated (LGI) proteins play important roles in nervous system development and mutations in their genes have been associated with epilepsy and amyelination. Their function involves interactions with ADAM22 and ADAM23 cell surface receptors, possibly in apposing membranes, thus attenuating cellular interactions. LGI4-ADAM22 interactions are required for axonal sorting and myelination in the developing peripheral nervous system (PNS). Functional analysis revealed that, despite their high homology and affinity for ADAM22, LGI proteins are functionally distinct. To dissect the key residues in LGI proteins required for coordinating axonal sorting and myelination in the developing PNS, we adopted a phylogenetic and computational approach and demonstrate that the mechanism of action of LGI4 depends on a cluster of three amino acids on the outer surface of the LGI4 protein, thus providing a structural basis for the mechanistic differences in LGI protein function in nervous system development and evolution

Functional phylogenetic analysis of LGI proteins identifies an interaction motif crucial for myelination

The cellular interactions that drive the formation and maintenance of the insulating myelin sheath around axons are only partially understood. Leucine-rich glioma-inactivated (LGI) proteins play important roles in nervous system development and mutations in their genes have been associated with epilepsy and amyelination. Their function involves interactions with ADAM22 and ADAM23 cell surface receptors, possibly in apposing membranes, thus attenuating cellular interactions. LGI4-ADAM22 interactions are required for axonal sorting and myelination in the developing peripheral nervous system (PNS). Functional analysis revealed that, despite their high homology and affinity for ADAM22, LGI proteins are functionally distinct. To dissect the key residues in LGI proteins required for coordinating axonal sorting and myelination in the developing PNS, we adopted a phylogenetic and computational approach and demonstrate that the mechanism of action of LGI4 depends on a cluster of three amino acids on the outer surface of the LGI4 protein, thus providing a structural basis for the mechanistic differences in LGI protein function in nervous system development and evolution

Functional phylogenetic analysis of LGI proteins identifies an interaction motif crucial for myelination

The cellular interactions that drive the formation and maintenance of the insulating myelin sheath around axons are only partially understood. Leucine-rich glioma-inactivated (LGI) proteins play important roles in nervous system development and mutations in their genes have been associated with epilepsy and amyelination. Their function involves interactions with ADAM22 and ADAM23 cell surface receptors, possibly in apposing membranes, thus attenuating cellular interactions. LGI4-ADAM22 interactions are required for axonal sorting and myelination in the developing peripheral nervous system (PNS). Functional analysis revealed that, despite their high homology and affinity for ADAM22, LGI proteins are functionally distinct. To dissect the key residues in LGI proteins required for coordinating axonal sorting and myelination in the developing PNS, we adopted a phylogenetic and computational approach and demonstrate that the mechanism of action of LGI4 depends on a cluster of three amino acids on the outer surface of the LGI4 protein, thus providing a structural basis for the mechanistic differences in LGI protein function in nervous system development and evolution

Results from the NASA Capability Roadmap Team for In-Situ Resource Utilization (ISRU)

On January 14, 2004, the President of the United States unveiled a new vision for robotic and human exploration of space entitled, "A Renewed Spirit of Discovery". As stated by the President in the Vision for Space Exploration (VSE), NASA must "... implement a sustained and affordable human and robotic program to explore the solar system and beyond " and ".. .develop new technologies and harness the moon's abundant resources to allow manned exploration of more challenging environments." A key to fulfilling the goal of sustained and affordable human and robotic exploration will be the ability to use resources that are available at the site of exploration to "live off the land" instead of bringing everything from Earth, known as In-Situ Resource Utilization (ISRU). ISRU can significantly reduce the mass, cost, and risk of exploration through capabilities such as: mission consumable production (propellants, fuel cell reagents, life support consumables, and feedstock for manufacturing & construction); surface construction (radiation shields, landing pads, walls, habitats, etc.); manufacturing and repair with in-situ resources (spare parts, wires, trusses, integrated systems etc.); and space utilities and power from space resources. On January 27th, 2004 the President's Commission on Implementation of U.S. Space Exploration Policy (Aldridge Committee) was created and its final report was released in June 2004. One of the report's recommendations was to establish special project teams to evaluate enabling technologies, of which "Planetary in situ resource utilization" was one of them. Based on the VSE and the commission's final report, NASA established fifteen Capability Roadmap teams, of which ISRU was one of the teams established. From Oct. 2004 to May 2005 the ISRU Capability Roadmap team examined the capabilities, benefits, architecture and mission implementation strategy, critical decisions, current state-of-the-art (SOA), challenges, technology gaps, and risks of ISRU for future human Moon and Mars exploration. This presentation will provide an overview of the ISRU capability, architecture, and implementation strategy examined by the ISRU Capability Roadmap team, along with a top-level review of ISRU benefits, resources and products of interest, and the current SOA in ISRU processes and systems. The presentation will also highlight the challenges of incorporating ISRU into future missions and the gaps in technologies and capabilities that need to be filled to enable ISRU

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

DICER1 mutations in childhood cystic nephroma and its relationship to DICER1-renal sarcoma

The pathogenesis of cystic nephroma of the kidney has interested pathologists for over 50 years. Emerging from its initial designation as a type of unilateral multilocular cyst, cystic nephroma has been considered as either a developmental abnormality or a neoplasm or both. Many have viewed cystic nephroma as the benign end of the pathologic spectrum with cystic partially differentiated nephroblastoma and Wilms tumor, whereas others have considered it a mixed epithelial and stromal tumor. We hypothesize that cystic nephroma, like the pleuropulmonary blastoma in the lung, represents a spectrum of abnormal renal organogenesis with risk for malignant transformation. Here we studied DICER1 mutations in a cohort of 20 cystic nephromas and 6 cystic partially differentiated nephroblastomas, selected independently of a familial association with pleuropulmonary blastoma and describe four cases of sarcoma arising in cystic nephroma, which have a similarity to the solid areas of type II or III pleuropulmonary blastoma. The genetic analyses presented here confirm that DICER1 mutations are the major genetic event in the development of cystic nephroma. Further, cystic nephroma and pleuropulmonary blastoma have similar DICER1 loss of function and ‘hotspot' missense mutation rates, which involve specific amino acids in the RNase IIIb domain. We propose an alternative pathway with the genetic pathogenesis of cystic nephroma and DICER1-renal sarcoma paralleling that of type I to type II/III malignant progression of pleuropulmonary blastoma

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.Comment: 36 pages, 8 figures, 3 table