Stability and migration of slab-derived carbonate-rich melts above the transition zone

We present a theoretical model of the stability and migration of carbonate-rich melts to test whether they can explain seismic low-velocity layers (LVLs) observed above stalled slabs in several convergent tectonic settings. The LVLs, located atop the mantle transition zone, contain small (similar to 1 vol%) amounts of partial melt, possibly derived from melting of subducted carbonate-bearing oceanic crust. Petrological and geochemical evidence from inclusions in superdeep diamonds supports the existence of slab-derived carbonate melt, which may potentially explain the origin of the observed melt in the LVL. However, the presumptive reducing nature of the ambient mantle can be an impediment to the stability of carbonated melt. To reconcile this apparent contradiction, we test the stability and migration rates of carbonate-rich melts atop a stalled slab as a function of melt percolation, redox freezing, amount of carbon supplied by subduction, and the metallic Fe concentration in the mantle. Our results demonstrate that carbonaterich melts in the LVL can potentially survive redox freezing over long geological time scales. We also show that the amount of subducted carbon exerts a stronger influence on the stability of carbonate melt than does the mantle redox condition. Concentration dependent melt density leads to rapid melt propagation through channels while a constant melt density causes melt to migrate as a planar front. Our calculations suggest that the LVLs can sequester significant fractions of carbon transported to the mantle by subduction. (C) 2019 Elsevier B.V. All rights reserved

High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years, progress toward making high throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping of physician notes.Comment: Submitted to IEEE EMBS Summer conference 202

High Throughput Neurological Phenotyping with MetaMap

The phenotyping of neurological patients involves the conversion of signs and symptoms into machine readable codes selected from an appropriate ontology. The phenotyping of neurological patients is manual and laborious. MetaMap is used for high throughput mapping of the medical literature to concepts in the Unified Medical Language System Metathesaurus (UMLS). MetaMap was evaluated as a tool for the high throughput phenotyping of neurological patients. Based on 15 patient histories from electronic health records, 30 patient histories from neurology textbooks, and 20 clinical summaries from the Online Mendelian Inheritance in Man repository, MetaMap showed a recall of 61-89%, a precision of 84-93%, and an accuracy of 56-84% for the identification of phenotype concepts. The most common cause of false negatives (failure to recognize a phenotype concept) was an inability of MetaMap to find concepts that were represented as a description or a definition of the concept. The most common cause of false positives (incorrect identification of a concept in the text) was a failure to recognize that a concept was negated. MetaMap shows potential for high throughput phenotyping of neurological patients if the problems of false negatives and false positives can be solved

The visualization of Orphadata neurology phenotypes

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of neurologic disease is represented as a list of concepts of variable length where the concepts are selected from a restricted ontology. Visualizations of these concept lists are not provided. We address this limitation by using subsumption to reduce the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length vectors. Phenotype vectors were aggregated into matrices and visualized as heat maps that allowed side-by-side disease comparisons. Individual diseases (representing a row in the matrix) were visualized as word clouds. We illustrate the utility of this approach by visualizing the neuro-phenotypes of 32 dystonic diseases from Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds

Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records

The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting signs and symptoms from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators

Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis

Evidence of Volatile-Induced Melting in the Northeast Asian Upper Mantle

International audienc

Frozen section analysis and sentinel lymph node biopsy in well differentiated thyroid cancer

BACKGROUND: The aim of this study is to prospectively review the role of sentinel lymph node (SLN) biopsy in the management of well differentiated thyroid carcinoma (WDTC), and to determine the efficacy of intraoperative frozen section analysis at detecting SLN metastasis and central compartment involvement. METHODS: The SLN biopsy protocol using 1% methylene blue was performed in 300 patients undergoing thyroidectomy for WDTC. A limited pretracheal central compartment neck dissection (CCND) was performed on all patients. Lymph nodes staining blue were considered as SLN’s. Both frozen and permanent section analyses were performed. RESULTS: SLN’s with metastasis were found in 14.3% (43/300) of cases. Of this, 11% (33/300) were positive on intraoperative frozen section analysis. Frozen section results failed in predicting central compartment involvement in 15 cases (5%) whereas central neck compartment involvement was missed in 5 cases (1.7%) when based on permanent section results. On frozen section analysis, the sensitivity, specificity, positive predictive value and negative predictive value (95% CI) of our SLN biopsy technique aiming to remove all disease from the central compartment was 68.8% (53.6-80.9), 100% (98.1-100), 100% (87.0-100) and 94.4% (90.7-96.7) respectively with P < 0.0001. On permanent section analysis, the values were 89.6% (76.6-96.1), 100% (98.1-100), 100% (89.8-100), and 98.1% (95.3-99.3) with P < 0.0001. CONCLUSION: This data series demonstrates that patients with WDTC have positive SLN’s in 14.3% of cases. Moreover, when the SLN’s are negative for metastasis on frozen section, the central compartment was disease-free in 94.4% of cases. Finally, this study shows that 23.3% of positive SLN’s were false negatives on intraoperative frozen section. According to this data, SLN involvement is an accurate predictor of central compartment metastasis, however surgeons should use caution when relying on intraoperative frozen section to determine whether to perform a CCND