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Medical Data Architecture Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current International Space Station (ISS) medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable an increasingly autonomous crew than the current ISS paradigm. The MDA will develop capabilities that support automated data collection, and the necessary functionality and challenges in executing a self-contained medical system that approaches crew health care delivery without assistance from ground support.To attain this goal, the first year of the MDA project focused on reducing technical risk, developing documentation and instituting iterative development processes that established the basis for the first version of MDA software (or Test Bed 1). Test Bed 1 is based on a nominal operations scenario authored by the ExMC Element Scientist. This narrative was decomposed into a Concept of Operations that formed the basis for Test Bed 1 requirements. These requirements were successfully vetted through the MDA Test Bed 1 System Requirements Review, which permitted the MDA project to begin software code development and component integration. This paper highlights the MDA objectives, development processes, and accomplishments, and identifies the fiscal year 2017 milestones and deliverables in the upcoming year.

Exploration medical system↗

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗

Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA matures—including the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missions—project focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided.

Krihak, M.↗

Plakophilin3 loss leads to an increase in lipocalin2 expression, which is required for tumour formation

Highlights: • Plakophilin3 loss leads to an increase in LCN2 expression in multiple cell types. • The increase in LCN2 expression is dependent on the activity of p38 MAPK. • Plakophilin3 inhibits LCN2 expression by restricting p38 MAPK to the cytoplasm. • The increase in LCN2 expression is required for tumour formation upon plakophilin3 loss. An increase in tumour formation and metastasis are observed upon plakophilin3 (PKP3) loss. To identify pathways downstream of PKP3 loss that are required for increased tumour formation, a gene expression analysis was performed, which demonstrated that the expression of lipocalin2 (LCN2) was elevated upon PKP3 loss and this is consistent with expression data from human tumour samples suggesting that PKP3 loss correlates with an increase in LCN2 expression. PKP3 loss leads to an increase in invasion, tumour formation and metastasis and these phenotypes were dependent on the increase in LCN2 expression. The increased LCN2 expression was due to an increase in the activation of p38 MAPK in the HCT116 derived PKP3 knockdown clones as LCN2 expression decreased upon inhibition of p38 MAPK. The phosphorylated active form of p38 MAPK is translocated to the nucleus upon PKP3 loss and is dependent on complex formation between p38 MAPK and PKP3. WT PKP3 inhibits LCN2 reporter activity in PKP3 knockdown cells but a PKP3 mutant that fails to form a complex with p38 MAPK cannot suppress LCN2 promoter activity. Further, LCN2 expression is decreased upon loss of p38β, but not p38α, in the PKP3 knockdown cells. These results suggest that PKP3 loss leads to an increase in the nuclear translocation of p38 MAPK and p38β MAPK is required for the increase in LCN2 expression.

60 APPLIED LIFE SCIENCES↗

Bioinformatics Resource Manager

The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of miRNA-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers.

Brown, Joseph↗

Project WISH: The Emerald City

Project WISH (Wandering Interplanetary Space Harbor) is a three-year design effort currently being conducted at The Ohio State University. Its goal is the design of a space oasis to be used in the exploration of the solar system during the midtwenty-first century. This spacecraft, named Emerald City, is to conduct and provide support for missions to other planetary bodies with the purpose of exploration, scientific study, and colonization. It is to sustain a crew of between 500 and 1000 people at a time, and be capable of traveling from a nominal orbit to the planets in reasonably short flight times. Such a ship obviously presents many technical and design challenges, some of which were examined through the course of Project WISH. This year, Phase 2 (1990-1991) of Project WISH was carried out. The basic design of the Emerald City resulting from Phase 1 (1989-1990) was taken and improved upon through more detailed analysis and revision. At the core of this year's study were orbital mechanics, propulsion, attitude control, and human factors. Throughout the year, these areas were examined and information was compiled on their technologies, performances, and relationships. Then, using the data obtained through these studies, two specific missions were designed: an envelope mission from a nominal orbit of 4 AU to Saturn and a single point design for a specific mission from the Earth to Mars. The latter was designed in view of the special interest that Mars is attracting for near-future space exploration. The mission to Saturn has all the first six planets within its flight envelope in less than or equal to a 3-year flight time at any time upon demand, and it has Uranus in its flight envelope most of the time upon demand. These mission studies provided data on the approximate size, weight, number of engines, and other important design values that would be required for the Emerald City.

Source record↗

Large Area Lunar Dust Flux Measurement Instrument

The instrument under development is designed to characterize the flux and size distribution of the lunar micrometeoroid and secondary ejecta environment. When deployed on the lunar surface, the data collected will benefit fundamental lunar science as well as enabling more reliable impact risk assessments for human lunar exploration activities. To perform this task, the instrument requirements are demanding. It must have as large a surface area as possible to sample the very sparse population of the larger potentially damage-inducing micrometeorites. It must also have very high sensitivity to enable it to measure the flux of small (<10 micron) micrometeorite and secondary ejecta dust particles. To be delivered to the lunar surface, it must also be very low mass, rugged and stow compactly. The instrument designed to meet these requirements is called FOMIS. It is a large-area thin film under tension (i.e. a drum) with multiple fiber optic displacement (FOD) sensors to monitor displacements of the film. This sensor was chosen since it can measure displacements over a wide dynamic range: 1 cm to sub-Angstrom. A prototype system was successfully demonstrated using the hypervelocity impact test facility at the University of Kent (Canterbury, UK). Based on these results, the prototype system can detect hypervelocity (approx.5 km/s) impacts by particles as small as 2 microns diameter. Additional tests using slow speeds find that it can detect secondary ejecta particles (which do not penetrate the film) with momentums as small as 15 pico-gram 100m/s, or nominally 5 microns diameter at 100 m/s.

Corsaro, R.↗

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗

Exploring Energy-Efficient and Sustainable Urban Mobility Strategies: An Initial Framework to Curate Data/Models, Measure Performance, and Diffuse Innovation: Preprint

Many cities across the United States seek to understand the maturity of data and models that are available to help manage challenges, opportunities, and uncertainties associated with the shifts in technologies, human behaviors and sustainable urban mobility strategies. One key question identified for smart city action planning includes how to best shape continuous improvements for urban populations at the intersection of mobility, energy, and quality of life? With the emerging megatrends of urbanization (more than 70% of world population in cities by 2050), on-demand shared mobility, vehicle electrification, and automated vehicles, initial 'urban science' studies to date have demonstrated the potential and need for maturing the related data and model ecosystems and on-going performance measurement across multiple urban system goals: e.g., from more mobility, clean and efficient energy use, accessibility and safety to less air pollution, traffic, and resource-intensive urban sprawl. To build on emerging literature and understand city responses to disruptive change, this initial study engages researchers and practitioners across four smart city finalists (Columbus, Denver, Austin and Portland) that competed in the U.S. Department of Transportation (DOT) Smart City Challenge. The initial results emphasize the need for a suite of datasets and diverse analytical approaches that support U.S. Department of Energy (DOE) relevant research with cities. Considering desirable energy and mobility outcomes as a first step to advancing smart city solutions strategies, we systematically review approaches of and shortcomings in four U.S. cities, and suggest improvements in three areas: measurement, modeling effectiveness of new mobility technologies, and data-driven governance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Recent Advances in Analytical Electron Microscopy for Irradiated Materials

Analytical electron microscopy (AEM) in the scanning transmission electron microscope (STEM) has long been a staple of post-irradiation examination (PIE) and materials development campaigns. Recently, new advances in both STEM hardware and post-processing data-analysis software, along with ancillary advances such as in focused ion beam (FIB) sample preparation, has led to exciting advances in the quantity and quality of data that can be obtained. This talk will summarize recent experiments performed at Oak Ridge National Laboratory (ORNL), especially work on neutron-irradiated materials and fuels taking advantage of a newly-installed latest-generation AEM tool in the ORNL irradiated materials laboratories. ORNL installed a new FEI Talos F200X microscope, a 200 keV, high-brightness Schottky emission AEM system optimized for high-speed, high-efficiency X-ray mapping experiments. The F200X features the FEI 'SuperX' four-sector silicon drift detector (SDD) X-ray detector, which provides 0.9 sr solid angle of collection. By combining the high-brightness electron source, latest-generation electron optics, and fast computers and pulse processors, X-ray mapping speed is increased by a factor of 50-100 x compared to the previous-generation instruments. In addition to improvements in hardware, data mining via multivariate statistical analysis (MVSA) allows for maximum insight to the data obtained from the new hardware. MVSA data mining of the X-ray maps allows, for instance, unbiased computational identification of the phases present, and discovery of small phases or precipitates not expected by the human analyst. This talk will present examples of experiments performed using the most modern AEM tools and software. Silicon carbide is a major candidate for next-generation nuclear applications. Nano-infiltration by transient eutectic (NITE) SiC requires low sintering temperatures. X-ray mapping of the NITE structure find pockets of different oxides, such as Zr-Y-oxide, Y-Al-oxide, and yttria. MVSA data mining finds the regions where different oxides are present and indicates that Y additions preferentially wet grain boundaries, where Zr additions are preferentially concentrated at grain triple points. SiC coatings from AGR-I experiment TRISO particles were found to contain significant quantities of fission products (e.g., Pd) as well as actinides (U, Pu). MVSA data mining found complex precipitates and grain boundary films of the fission products and actinides deep (∼several microns) into the SiC layer. Advanced AEM methods, combined with MVSA data mining, makes possible both rapid and detailed PIE. Ferritic alloys, such as accident-tolerant FeCrAl grades, benefit greatly from advanced AEM examination. Neutron-irradiated FeCrAl precipitates the detrimental Cr-rich α' phase. Because the SuperX SDD system has four detectors, it is possible to obtain high X-ray count rates even on a highly tilted specimen, as might be needed to obtain STEM images of dislocation loops. Results on neutron-irradiated FeCrAl indicates that α' size and number density can be obtained by AEM, and MVSA results determine that minor elements (such as Si) has complex radiation-induced segregation behavior at grain boundaries. We have also developed and validated computer models of the AEM experiment, allowing different microstructures' X-ray map results to be simulated for different STEM instruments. This allows X-ray mapping to be more quantitative, such as by estimating what size nanoclusters in an oxide-dispersion strengthened steel would be visible or invisible in STEM. In summary, new advanced AEM technology provides a major leap forward in experiments possible in PIE of irradiated materials. (authors)

36 MATERIALS SCIENCE↗

Illumination requirements for operating a space remote manipulator

Critical issues and requirements involved in illuminating remote manipulator operations in space help establish engineering designs for these manipulators. A remote manipulator is defined as any mechanical device that is controlled indirectly or from a distance by a human operator for the purpose of performing potentially dangerous or hazardous tasks to increase safety, reliability, and efficiency. Future space flights will rely on remote manipulators for a variety of tasks including satellite repair and servicing, structural assembly, data collection and analysis, and performance of contingency tasks. Carefully designed illumination of these manipulators will assure that these tasks will be completed efficiently and successfully. Studies concerning the influence of illumination on operation of a remote manipulator are few. Available results show that illumination can influence how successfully a human operates a remote manipulator. The intent of this study was to more fully examine this topic.

Chandlee, George O.↗

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗

Simplifying the Tracking of Waste Disposal Inventories: Process Improvement for Rapid Data Interpretation and Reduced Errors - 16143

Tracking the inventory of low-level radioactive waste at an operating disposal facility offers unique challenges. At the Savannah River Site's Saltstone Disposal Facility (SDF) the inventory used in Performance Assessment modeling is determined based on radionuclide concentrations from the aqueous waste in a feed tank and the volume of the waste that is transferred from the feed tank to the Saltstone Production Facility (SPF). These inputs are reported quarterly. As such, the disposal inventories are also updated quarterly; however, due to decay and ingrowth, the inventory values for some radionuclides can be misrepresented if the data is not tracked appropriately. Additionally, it is not feasible to analyze the feed tank samples for every possible radionuclide; therefore, a series of adjustments are necessary to estimate the concentrations of radionuclides that are not explicitly measured but still pose a potential dose risk. Prior to 2015, the SDF disposal inventories were updated using a series of spreadsheets that required users to perform a significant number of steps (including manually transferring data back and forth between a separate decay and ingrowth calculation software). Despite this labor intensive approach, the process only provided single point-in-time results. With multiple steps, minimal automation, and limited result data, this approach to inventory tracking was not ideal and minor mistakes were often identified during the equally cumbersome review process. As a process improvement, the simplified SDF Waste Inventory Disposed Estimator (SDF-WIDE) Model was developed using GoldSim software. The software allows users to identify specific dates to define the duration of the simulation, from the beginning of operations until the time of future facility closure, and provides graphical interfaces for displaying data over time. Further, GoldSim's Contaminant Transport Module is used to perform the decay and ingrowth calculations, making the model completely self-contained (i.e., there is no need to transfer intermediate data back and forth between different programs). GoldSim also includes model 'versioning' features that significantly reduce the time needed to perform the appropriate quality assurance reviews as required for each quarterly inventory update. By transitioning from the complex spreadsheets to the simplified SDFWIDE model, the time needed to perform updates to the SDF inventory has been reduced from days to hours, the risk of making human errors has been greatly mitigated, and users are able to quickly observe trends in the data over time. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Testing of Advanced Capabilities to Enable In-time Safety Management and Assurance for Future Flight Operations

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

sUAS↗

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

42 ENGINEERING↗

Phase Mapper: Accelerating Materials Discovery with AI

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

Computer Science↗

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions

Understanding protein-protein interactions (PPIs) between viruses and host organisms is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPIs by utilizing models trained on better-studied virus-human and human-human PPIs. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93–99 %) and AUPRC scores (0.8–0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance using standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, resulting in a drop in accuracy below 50 %. We propose a viral protein-specific evaluation framework that categorizes viral proteins into majority and minority classes based on their representation in the dataset, enabling comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

59 BASIC BIOLOGICAL SCIENCES↗