Detailed information about landslide occurrence is the foundation for advancing process understanding, susceptibility mapping, and risk reduction. Despite the recent revolution in digital elevation ...data and remote sensing technologies, landslide mapping remains resource intensive. Consequently, a modern, comprehensive map of landslide occurrence across the United States (USA) has not been compiled. As a first step toward this goal, we present a national-scale compilation of existing, publicly available landslide inventories. This geodatabase can be downloaded in its entirety or viewed through an online, searchable map, with parsimonious attributes and direct links to the contributing sources with additional details. The mapped spatial pattern and concentration of landslides are consistent with prior characterization of susceptibility within the conterminous USA, with some notable exceptions on the West Coast. Although the database is evolving and known to be incomplete in many regions, it confirms that landslides do occur across the country, thus highlighting the importance of our national-scale assessment. The map illustrates regions where high-quality mapping has occurred and, in contrast, where additional resources could improve confidence in landslide characterization. For example, borders between states and other jurisdictions are quite apparent, indicating the variation in approaches to data collection by different agencies and disparity between the resources dedicated to landslide characterization. Further investigations are needed to better assess susceptibility and to determine whether regions with high relief and steep topography, but without mapped landslides, require further landslide inventory mapping. Overall, this map provides a new resource for accessing information about known landslides across the USA.
The QuantiFERON-TB Gold Plus (QFT-Plus; Qiagen, Germantown, MD) interferon gamma release assay (IGRA) received FDA clearance in 2017 and will replace the prior version of the assay, the QFT-Gold ...In-Tube (QFT-GIT). Here, we compared performances of the QFT-Plus assay and the QFT-GIT version in a diverse patient population, including patients undergoing evaluation for or follow-up of latent tuberculosis infection (LTBI;
= 39) or active TB infection (
= 3), and in health care workers (HCWs;
= 119) at Mayo Clinic (Rochester, MN). Compared to the QFT-GIT, the QFT-Plus assay showed 91.2% (31/34) positive, 98.4% (124/126) negative, and 96.6% (156/161) overall qualitative agreement among the 161 enrolled subjects, with a Cohen's kappa value of 0.91 (excellent interrater agreement). Among the 28 patients diagnosed with LTBI at the time of enrollment, the QFT-GIT and QFT-Plus assays agreed in 24 (85.7%) patients; in all four discordant patients, the positivity of the QFT-GIT or QFT-Plus IGRA was associated with low-level interferon gamma (IFN-γ) reactivity, ranging from 0.36 IU/ml to 0.66 IU/ml. Additionally, we document a high degree of correlation between IFN-γ levels in the QFT-GIT TB antigen tube and each of the two QFT-Plus TB antigen tubes, as well as between the QFT-Plus TB1 and TB2 tubes (Pearson's correlation coefficients
> 0.95). Overall, we show comparable results between the QFT-GIT and QFT-Plus assays in our study population composed of subjects presenting with a diverse spectrum of TB infections. Our findings suggest that the necessary transition to the QFT-Plus assay will be associated with a minimal difference in assay performance characteristics.
Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex ...heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.
Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize ...multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin-angiotensin-aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.
Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves ...interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi.
A four-step process to generate 8-oxa-3-aza-bicyclo3.2.1octane hydrochloride starting with 5-hydroxymethyl-2-furfuraldehyde is described. Raney nickel-mediated reduction of ...5-hydroxymethyl-2-furfuraldehyde afforded 2,5-bis(hydroxymethyl)tetrahydrofuran with a predominantly cis configuration. Conversion of the diol to a ditosylate, followed by cyclization with benzylamine generated the 8-oxa-3-aza-bicyclo3.2.1octane core. The title compound was prepared via hydrogenolysis of the N-benzyl group with Pearlman’s catalyst and isolated as the hydrochloride salt from a mixture of 2-propanol and heptane. The overall yield for the process was 43−64%. An impurity generated during scale up highlighted the need for a strict in-process specification for acetonitrile content prior to performing the final hydrogenolysis. Ethanol conditioning of all processing equipment that had acetonitrile contact prior to the introduction of the substrate stock solution is highly recommended.
Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of ...nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.
Efflux of CO2 above releases of petroleum light nonaqueous phase liquids (LNAPLs) has emerged as a critical parameter for resolving natural losses of LNAPLs and managing LNAPL sites. Current ...approaches for resolving CO2 efflux include gradient, flux chamber, and mass balance methods. Herein a new method for measuring CO2 efflux above LNAPL bodies, referred to as CO2 traps, is introduced. CO2 traps involve an upper and a lower solid phase sorbent elements that convert CO2 gas into solid phase carbonates. The sorbent is placed in an open vertical section of 10 cm ID polyvinyl chloride (PVC) pipe located at grade. The lower sorbent element captures CO2 released from the subsurface via diffusion and advection. The upper sorbent element prevents atmospheric CO2 from reaching the lower sorbent element. CO2 traps provide integral measurement of CO2 efflux based over the period of deployment, typically 2 to 4 weeks. Favorable attributes of CO2 traps include simplicity, generation of integral (time averaged) measurement, and a simple means of capturing CO2 for carbon isotope analysis. Results from open and closed laboratory experiments indicate that CO2 traps quantitatively capture CO2 . Results from the deployment of 23 CO2 traps at a former refinery indicate natural loss rates of LNAPL (measured in the fall, likely concurrent with high soil temperatures and consequently high degradation rates) ranging from 13,400 to 130,000 liters per hectare per year (L/Ha/year). A set of field triplicates indicates a coefficient of variation of 18% (resulting from local spatial variations and issues with measurement accuracy).
Enhanced thermal conductivity uranium dioxide composites containing silicon carbide (UO2−SiC) and diamond (UO2-diamond) have been irradiated to low burnup. The conditions of this irradiation test and ...subsequent postirradiation examinations are discussed. These irradiations evaluate fuel microstructure and potential fuel cladding interaction of UO2 composites, which have been proposed as accident tolerant fuel candidates.
Both non-destructive and destructive techniques have been used to evaluate fuel integrity, fission gas release, fission product distribution, burnup, fuel swelling and cladding strain. Examination of the UO2-SiC pellets revealed enhanced cracking when compared to UO2 pellets irradiated under similar conditions. Instability of the SiC whiskers in the uranium dioxide matrix was observed in the pellet central region, where the local temperatures exceeded 1300°C. The microstructure of the UO2-diamond was severely disrupted during irradiation, resulting in local migration of cesium along the fuel stack and increased fission gas release when compared with the expected release from the Vitanza curve at corresponding values of burnup and irradiation temperature.
The postirradiation examination results cast doubt on the suitability of these additives to improve UO2 fuel performance in a way that would lead to enhanced accident tolerance.
Abstract Background Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast‐enhanced computed tomography (CT). There is a need for an ...objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams. Purpose The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast‐enhanced abdomen CT examinations. Methods Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast‐enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations. Results The best classifier was found to be a random forest, with a precision of 0.892 in the held‐out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU. Conclusions The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time‐consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast‐enhanced CT examinations.
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