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492 records · Page 28

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DOE OSTI2024

HSQC spectra of lignin isolated from poplar roots

Here we present a curated dataset of two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra of lignin isolated from roots of a greenhouse grown natural population of an energy crop poplar (Populus trichocarpa). Dormant cuttings of field-grown poplar were grown in 6-liter pots in a peat-based media containing bark, perlite, vermiculite, dolomite lime and a wetting agent in an environmentally controlled greenhouse. Temperatures were between 21 and 23 °C, with supplemental lighting to support a 16-h day length using 1000-watt high-pressure sodium lights in greenhouse. Once established, all plants were cut-back, allowed to regrow and harvested at the same time following an eight-month long growth period. Plants were harvested and the belowground roots were washed off soils, blotted, dried in an oven at 70 °C for 3 days, and Wiley milled (mesh size 20). The roots were Soxhlet-extracted with toluene/ethanol for 24 h to remove extractives. The extracted roots were ball-milled in a Retsch PM100 planetary ball mill using a porcelain jar with ceramic balls at 600 rpm for 2 h (in 5 min on and 5 min off cycles to avoid excessive sample heating). The ball-milled materials were then subjected to enzymatic hydrolysis for 48 h followed by centrifugation and washing with deionized water. The solid residue was extracted twice with 96% (v/v) 1,4-dioxane/water mixture at room temperature overnight. The extracts were combined, rotary evaporated, and freeze-dried to recover lignin. The dry lignin samples were dissolved in deuterated dimethyl sulfoxide (d6) and transferred into a 5 mm tube. 13C–1H HSQC experiments were performed in a Bruker Avance III HD 500 MHz NMR spectrometer operating at a frequency of 125.12 MHz for the 13C nucleus using a standard Bruker pulse sequence on a Prodigy platform cryoprobe. The NMR spectra were acquired under the following acquisition conditions: 220 ppm spectral width in F1 (13C) dimension with 256 data points and 12 ppm spectral width in F2 (1H) dimension with 1024 data points, a 90° pulse, a one bond C–H coupling constant of 145 Hz, a 1.0 s pulse delay, and 64 scans. Spectra were processed using the Bruker TopSpin software. Additional meta data is embedded in the raw spectra figures.

HSQC, lignin, poplar, roots, CBI↗

NASA NTRS2015

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

proteomics↗

DOE OSTI2021

The use and abuse of cinnabar in Late Neolithic and Copper Age Iberia

Abstract In this study, total mercury (THg) was analyzed in archaeological human bone from 23 sites dating to between the Middle Neolithic and the Antiquity. A total of 370 individuals from individual or collective burials was sampled, mostly using cortical bone from the humerus. These individuals were recovered from over 50 different funerary structures ranging from tholoi, pits, caves, and hypogea. Although cinnabar (HgS) is a likely cause of mercury poisoning and toxicity for people exposed to this mineral from mining or use as a paint or pigment, not all sites investigated here had cinnabar associated with the burials or other excavated areas. We found unusual levels of THg in many of the sampled individuals that we assume were caused by exposure to cinnabar in life, and not by diagenetic processes or other exposures to mercury such as through diet, which would only cause negligible accumulation of THg in bone. Our data, based on the largest sampling ever undertaken on contamination of human bone through archaeological evidence, provide a baseline for additional research on cinnabar and its use in Prehistory. Moderate to high levels of THg in human bone are mainly associated with societies dating from the second half of the 4th to late 3rd millennia B.C. (Late Neolithic to Middle Chalcolithic) in southern Iberia. By the Late Chalcolithic and Early Bronze Age, the use of cinnabar decreased significantly and became minimal or absent. The use and abuse of cinnabar appears to have been pervasive throughout the above‐mentioned period and particularly between c. 2900–2300 B.C. This occurred in connection with the high symbolic and probably sacred value of the substance, which was sought after, traded, and extensively used in a variety of rituals and social practices.

Emslie, Steven D.↗

DOE OSTI2019

Diagnostic performance of MRI- versus MDCT-categorized T3cd/T4 for identifying high-risk stage II or stage III colon cancers: a pilot study

Purpose: The aim of this study was to determine the diagnostic performance of magnetic resonance imaging (MRI)-categorized T3cd/T4 tumors for identifying high-risk stage II or stage III cancer in patients with curatively resectable colon cancer in comparison to that of multidetector computed tomography (MDCT). Materials and Methods: Thirty-eight patients with histopathologically indicated adenocarcinomas prospectively underwent MRI of the colon. Two radiologists independently and retrospectively assessed for T-category, including T3 substage (≤ T3ab vs. ≥ T3cd). The diagnostic accuracies and interreader agreements between assessments using each modality were compared using a pairwise comparison of receiver-operating characteristic curves and a weighted κ statistic, respectively. Results: Twenty-nine patients (76.3%) were histopathologically diagnosed with high-risk stage II or stage III colon cancer. The false-positive rate with MRI was lower than that with MDCT (0% vs. 7.9% for reader 1, 2.6% vs. 10.6% for reader 2). The diagnostic performance of MRI was better than that of MDCT across both readers (AUC: 0.707 vs. 0.506 [P = 0.032] for reader 1, 0.651 vs. 0.485 [P = 0.055] for reader 2). Moreover, MRI interreader agreement for the assessment of T3cd/T4 was significantly better than that of MDCT (κ = 0.821 vs. 0.391 [P = 0.017]). Conclusion: The diagnostic performance of MR imaging of the colon may be better than that of MDCT for identifying high-risk stage II or stage III cases. Particularly, colon MRI reduced the false-positive rate and improved the interreader agreement, although further studies with a larger sample size are required.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗