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At least 145 records · Page 8

Microbial community proteomics for characterizing the range of metabolic functions and activities of human gut microbiota

We found that the human gastrointestinal (GI) tract is a complex, dynamic ecosystem that consists of a carefully tuned balance of human host and microbiota membership. The microbiome component is not insignificant, but rather provides important functions that are absolutely critical to many aspects of human health, including nutrient transformation and absorption, drug metabolism, pathogen defense, and immune system development. Microbial community proteomics (sometimes referred to as metaproteomics) provides a powerful approach to measure the range and details of human gut microbiota functions and metabolic activities, revealing information about microbiome development and stability especially with regard to human health vs. disease states. In most cases, both microbial and human proteins are extracted from fecal samples and then measured by the high performance MS-based proteomics technology. We review the field of human gut microbiome community proteomics, with a focus on the experimental and informatics considerations involved in characterizing systems that range from low complexity defined model gut microbiota in gnotobiotic mice, to the simple gut microbiota in the GI tract of newborn infants, and finally to the complex gut microbiota in adults. Moreover, the current state-of-the-art in experimental and bioinformatics capabilities for community proteomics enable a detailed measurement of the gut microbiota, yielding valuable insights into the broad functional profiles of even complex microbiota. Future developments are likely to expand into improved analysis throughput and coverage depth, as well as post-translational modification characterizations.

59 BASIC BIOLOGICAL SCIENCES↗

Combining computational modeling and experimental library screening to affinity-mature VEEV-neutralizing antibody F5

Engineered monoclonal antibodies have proven to be highly effective therapeutics in recent viral outbreaks. However, despite technical advancements, an ability to rapidly adapt or increase antibody affinity and by extension, therapeutic efficacy, has yet to be fully realized. We endeavored to stand-up such a pipeline using molecular modeling combined with experimental library screening to increase the affinity of F5, a monoclonal antibody with potent neutralizing activity against Venezuelan Equine Encephalitis Virus (VEEV), to recombinant VEEV (IAB) E1E2 antigen. We modeled the F5/E1E2 binding interface and generated predictions for mutations to improve binding using a Rosetta-based approach and dTERMen, an informatics approach. The modeling was complicated by the fact that a high-resolution structure of F5 is not available and the H3 loop of F5 exceeds the length for which current modeling approaches can determine a unique structure. A subset of the predicted mutations from both methods were incorporated into a phage display library of scFvs. This library and a library generated by error-prone PCR were screened for binding affinity to the recombinant antigen. Results from the screens identified favorable mutations which were incorporated into 12 human-IgG1 variants. The best variant, containing eight mutations, improved KD from 0.63 nM (parental) to 0.01 nM. While this did not improve neutralization or therapeutic potency of F5 against IAB, it did increase cross-reactivity to other closely related VEEV epizootic and enzootic strains, demonstrating the potential of this method to rapidly adapt existing therapeutics to emerging viral strains.

affinity-maturation↗

FREDA: A Web Application for the Processing, Analysis, and Visualization of Fourier‐Transform Mass Spectrometry Data

The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).

47 OTHER INSTRUMENTATION↗

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗

A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Patient-Centered Radiology with FHIR: an Introduction to the Use of FHIR to Offer Radiology a Clinically Integrated Platform

Fast Healthcare Interoperability Resources (FHIR) is an open interoperability standard that allows external software to quickly search for and access clinical information from the electronic medical record (EMR) in a method that is developer-friendly, using current internet technology standards. In this article, we highlight the new FHIR standard and illustrate how FHIR can be used to offer the field of radiology a more clinically integrated and patient-centered system, opening the EMR to external radiology software in ways unfeasible with traditional standards. We explain how to construct FHIR queries relevant to medical imaging using the Society for Imaging Informatics in Medicine (SIIM) Hackathon application programming interface (API), provide sample queries for use, and suggest solutions to offer a patient-centered, rather than an image-centered, workflow that remains clinically relevant.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System

Open-source development can provide a platform for innovation by seeking feedback from community members as well as providing tools and infrastructure to test new standards. Vendors of proprietary systems may delay adoption of new standards until there are sufficient incentives such as legal mandates or financial incentives to encourage/mandate adoption. Moreover, open-source systems in healthcare have been widely adopted in low- and middle-income countries and can be used to bridge gaps that exist in global health radiology. Since 2011, the authors, along with a community of open-source contributors, have worked on developing an open-source radiology information system (RIS) across two communities—OpenMRS and LibreHealth. The main purpose of the RIS is to implement core radiology workflows, on which others can build and test new radiology standards. This work has resulted in three major releases of the system, with current architectural changes driven by changing technology, development of new standards in health and imaging informatics, and changing user needs. At their core, both these communities are focused on building general-purpose EHR systems, but based on user contributions from the fringes, we have been able to create an innovative system that has been used by hospitals and clinics in four different countries. We provide an overview of the history of the LibreHealth RIS, the architecture of the system, overview of standards integration, describe challenges of developing an open-source product, and future directions. Our goal is to attract more participation and involvement to further develop the LibreHealth RIS into an Enterprise Imaging System that can be used in other clinical imaging including pathology and dermatology.

61 RADIATION PROTECTION AND DOSIMETRY↗

Characterization of gamma-ray background outside of the High Flux Isotope Reactor

Static gamma-ray measurements collected between September 2017 and March 2018 in the vicinity of the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at Oak Ridge National Laboratory are presented. Temporal events are identified and tracked, including radioactive material transfers, rain events, and 41 Ar emissions. The remaining data is used to determine the average background and its variation. Finally, these results will be used to benchmark Monte Carlo transport models of the HFIR/REDC vicinity and provide training data for material transfer tracking algorithms and data fusion algorithms to determine HFIR operational status under the Multi-Informatics for Nuclear Operations Scenarios program.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric – number of objects and total surface – information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Data-driven discovery of a formation prediction rule on high-entropy ceramics

The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ B-TM ) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.

36 MATERIALS SCIENCE↗

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗

Diagonal entropy and topological phase transitions in extended Kitaev chains

We investigate the diagonal entropy for ground states of the extended Kitaev chains with extensive pairing and hopping terms. The systems contain rich topological phases equivalently represented by topological invariant winding numbers and Majorana zero modes. Both the finite size scaling law and block scaling law of the diagonal entropy are studied, which indicates that the diagonal entropy demonstrates volume effect. The parameter of volume term is regarded as the diagonal entropy density, which can identify the critical points of symmetry-protected topological phase transitions efficiently in the studied models, even for those with higher winding numbers. The formulation of block scaling law and the capability of diagonal entropy density in detecting topological phase transitions are independent of the chosen bases. In order to manifest the advantage of diagonal entropy, we also calculate the global entanglement, which cannot show clear signatures of the topological phase transitions. This work provides a new quantum-informatic approach to characterize the feature of the topologically ordered states and may motivate a deep understanding of the quantum coherence and diagonal entropy in various condensed matter systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

MiR-145 regulates PAK4 via the MAPK pathway and exhibits an antitumor effect in human colon cells

Highlights: Black-Right-Pointing-Pointer MiR-145 targets a putative binding site in the 3 Prime UTR of PAK4. Black-Right-Pointing-Pointer MiR-145 played an important role in inhibiting cell growth by directly targeting PAK4. Black-Right-Pointing-Pointer MiR-145 may function as tumor suppressors. -- Abstract: MicroRNAs (miRNAs) are regulators of numerous cellular events; accumulating evidence indicates that miRNAs play a key role in a wide range of biological functions, such as cellular proliferation, differentiation, and apoptosis in cancer. Down-regulated expression of miR-145 has been reported in colon cancer tissues and cell lines. The molecular mechanisms underlying miR-145 and the regulation of colon carcinogenesis remain unclear. In this study, we investigated the levels of miR-145 in human colon cancer cells using qRT-PCR and found markedly decreased levels compared to normal epithelial cells. We identified PAK4 as a novel target of miR-145 using informatics screening. Additionally, we demonstrated that miR-145 targets a putative binding site in the 3 Prime UTR of PAK4 and that its abundance is inversely associated with miR-145 expression in colon cancer cells; we confirmed this relationship using the luciferase reporter assay. Furthermore, restoration of miR-145 by mimics in SW620 cells significantly attenuated cell growth in vitro, in accordance with the inhibitory effects induced by siRNA mediated knockdown of PAK4. Taken together, these findings demonstrate that miR-145 downregulates P-ERK expression by targeting PAK4 and leads to inhibition of tumor growth.

60 APPLIED LIFE SCIENCES↗

MiR-181b targets Six2 and inhibits the proliferation of metanephric mesenchymal cells in vitro

Highlights: •We do bio-informatics websites to analysis of Six2 3′UTR. •MiR181b is a putative miRNA which can targets Six2 3′UTR. •MiR-181b binding site in the 3′UTR of Six2 is functional. •MiR-181b suppresses MK3 cells cell proliferation by targeting Six2. -- Abstract: MicroRNAs (miRNAs) are small non-coding RNAs that down-regulate gene expression by binding to target mRNA for cleavage or translational repression, and play important regulatory roles in renal development. Despite increasing genes have been predicted to be miRNA targets by bioinformatic analysis during kidney development, few of them have been verified by experiment. The objective of our study is to identify the miRNAs targeting Six2, a critical transcription factor that maintains the mesenchymal progenitor pool via self-renewal (proliferation) during renal development. We initially analyzed the 3′UTR of Six2 and found 37 binding sites targeted by 50 putative miRNAs in the 3′UTR of Six2. Among the 50 miRNAs, miR-181b is the miRNAs predicted by the three used websites. In our study, the results of luciferase reporter assay, realtime-PCR and Western blot demonstrated that miR-181b directly targeted on the 3′UTR of Six2 and down-regulate the expression of Six2 at mRNA and protein levels. Furthermore, EdU proliferation assay along with the Six2 rescue strategy showed that miR-181b suppresses the proliferation of metanephric mesenchymal by targeting Six2 in part. In our research, we concluded that by targeting the transcription factor gene Six2, miR-181b inhibits the proliferation of metanephric mesenchymal cells in vitro and might play an important role in the formation of nephrons.

60 APPLIED LIFE SCIENCES↗

Fibulin-4 is a novel Wnt/β-Catenin pathway activator in human osteosarcoma

Fibulin-4, an extracellular glycoprotein implicated in connective tissue development and elastic fiber formation, draws increasing focuses in cancer research. However, little is known about the underlying oncogenic roles of Fibulin-4 in human osteosarcoma (OS). In this study, by immunohistochemical analysis, upregulated expression of Fibulin-4 was found in the OS clinical specimens and cell lines compared to their normal counterparts. Fibulin-4 was positively correlated with the T stage of OS patients, and the proliferation index Ki67. Based on informatics analysis and functional verification, microRNA-137 was identified as a potential upstream regulator of Fibulin-4. Knockdown of Fibulin-4 or introduction of microRNA-137 inhibited cell proliferation and promoted cell apoptosis, and adverse effects were observed by overexpression of Fibulin-4. Furthermore, the tumor-suppressive functions of microRNA-137 were markedly abolished by restoration of Fibulin-4 expression in OS cells. Mechanistically, Fibulin-4 activated Wnt/β-Catenin pathway and promoted the expression of its downstream targets, including CCND2, c-Myc and VEGF. Taken together, Fibulin-4 plays critical neoplastic roles in tumor growth of human OS by activating Wnt/β-Catenin signaling and may represent a potential therapeutic target. -- Highlights: •Upregulated Fibulin-4 correlates tumor growth in human OS. •MicroRNA-137 is a critical regulator of Fibulin-4 expression. •Deregulated miR-137/Fibulin-4 axis promotes tumor growth of human OS. •Wnt/β-Catenin pathway is activated by Fibulin-4 stimulation.

60 APPLIED LIFE SCIENCES↗

The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.

97 MATHEMATICS AND COMPUTING↗

The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. Finally, the needed transformation of the LTG data culture requires emphasis in student education on science and management of data.

58 GEOSCIENCES↗

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗