Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Medical Illustration”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Protein Data Bank (PDB): Fifty-three years young and having a transformative impact on science and society

This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.

bioinformatics↗
DOE OSTI2020

Harvesting 62 Zn from an aqueous cocktail at the NSCL

“Isotope harvesting” is a technique that offers access to exotic radionuclides created as by-products during nuclear science research. Ongoing exploratory work at the National Superconducting Cyclotron Laboratory (NSCL) is directed towards the production and extraction of rare radionuclides from a flowing-water target and intends to pave the way for future harvesting efforts at the upcoming Facility for Rare Isotope Beams (FRIB). Here in this paper, we present the collection of 62 Zn from an aqueous matrix irradiated with a 150 MeV/nucleon 78Kr beam, while synergistically capturing other gaseous reaction products. In addition to the production rate for 62 Zn (9.08(30)E-5 62 Zn per incoming 78 Kr), the rates of formation for several other radionuclides were determined as well. The purification of 62 Zn from a large number of co-produced radionuclides was performed by anion exchange chromatography, allowing the isolation of 80.5(5.2) % of the generated 62 Zn. With the decay of 62 Zn the radioactive daughter 62 Cu is generated, and with the isolation of pure 62 Cu eluate, the principle of a medical radionuclide generator could be demonstrated. To illustrate the applicability of the obtained 62 Zn, the isolated product was used in free and DTPA-labelled form in a proof of principle plant uptake study with garden cress employing phosphor imaging for visualization.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Evaluation Challenges for the Application of Extended Reality Devices in Medicine

Augmented and virtual reality devices are being actively investigated and implemented for a wide range of medical uses. However, significant gaps in the evaluation of these medical devices and applications hinder their regulatory evaluation. Addressing these gaps is critical to demonstrating the devices’ safety and effectiveness. We outline the key technical and clinical evaluation challenges discussed during the US Food and Drug Administration’s public workshop, “Medical Extended Reality: Toward Best Evaluation Practices for Virtual and Augmented Reality in Medicine” and future directions for evaluation method development. Evaluation challenges were categorized into several key technical and clinical areas. Finally, we highlight current efforts in the standards communities and illustrate connections between the evaluation challenges and the intended uses of the medical extended reality (MXR) devices. Participants concluded that additional research is needed to assess the safety and effectiveness of MXR devices across the use cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and the inference on the parameters of interest — along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism. This report illustrates the most recent advancements in the area, and outlines, for each of the discussed applications as well as for automatic differentiation itself, ongoing and future work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Assessing the reliability of medical resource demand models in the context of COVID-19

Abstract Background Numerous medical resource demand models have been created as tools for governments or hospitals, aiming to predict the need for crucial resources like ventilators, hospital beds, personal protective equipment (PPE), and diagnostic kits during crises such as the COVID-19 pandemic. However, the reliability of these demand models remains uncertain. Methods Demand models typically consist of two main components: hospital use epidemiological models that predict hospitalizations or daily admissions, and a demand calculator that translates the outputs of the epidemiological model into predictions for resource usage. We conducted separate analyses to evaluate each of these components. In the first analysis, we validated various hospital use epidemiological models using a recent validation framework designed for epidemiological models. This allowed us to quantify the accuracy of the models in predicting critical aspects such as the date and magnitude of local COVID-19 peaks, among other factors. In the second analysis, we evaluated a range of demand calculators for ventilators, medical gowns, and COVID-19 test kits. To achieve this, we decoupled these demand calculators from the underlying epidemiological models and provided ground truth data for their inputs. This approach enabled a direct comparison of the demand calculators, comparing them against each other and actual usage data when available. The code is available athttps://doi.org/10.5281/zenodo.13712387. Results Performance varied greatly across the epidemiological models, with greater variability in COVID-19 hospital use predictions than for COVID-19 deaths as analyzed previously. Some models did not have any peaks. Among those that did, the models under-estimated date of peak approximately as often as they over-estimated, but were more likely to under-estimate magnitude of peak, with typical relative errors around 50%. Regarding demand calculator predictions, there was significant variability, including five-fold differences in predictions for gown models. Validation against actual or surrogate usage data illustrated the potential value of demand models while demonstrating their limitations. Conclusions The emerging field of demand modeling holds promise in averting medical resource shortages during future public health emergencies. However, achieving this potential necessitates focused efforts on standardization, transparency, and rigorous model validation before placing reliance on demand models in critical public health decision-making.

Medical Informatics↗
DOE OSTI2022

Antimicrobial Peptide Mimics for Clinical Use: Does Size Matter?

The search for efficient antimicrobial therapies that can alleviate suffering caused by infections from resistant bacteria is more urgent than ever before. Infections caused by multi-resistant pathogens represent a significant and increasing burden to healthcare and society and researcher are investigating new classes of bioactive compounds to slow down this development. Antimicrobial peptides from the innate immune system represent one promising class that offers a potential solution to the antibiotic resistance problem due to their mode of action on the microbial membranes. However, challenges associated with pharmacokinetics, bioavailability and off-target toxicity are slowing down the advancement and use of innate defensive peptides. Improving the therapeutic properties of these peptides is a strategy for reducing the clinical limitations and synthetic mimics of antimicrobial peptides are emerging as a promising class of molecules for a variety of antimicrobial applications. These compounds can be made significantly shorter while maintaining, or even improving antimicrobial properties, and several downsized synthetic mimics are now in clinical development for a range of infectious diseases. A variety of strategies can be employed to prepare these small compounds and this review describes the different compounds developed to date by adhering to a minimum pharmacophore based on an amphiphilic balance between cationic charge and hydrophobicity. These compounds can be made as small as dipeptides, circumventing the need for large compounds with elaborate three-dimensional structures to generate simplified and potent antimicrobial mimics for a range of medical applications. This review highlights key and recent development in the field of small antimicrobial peptide mimics as a promising class of antimicrobials, illustrating just how small you can go.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Programmable soft valves for digital and analog control

In soft devices, complex actuation sequences and precise force control typically require hard electronic valves and microcontrollers. Existing designs for entirely soft pneumatic control systems are capable of either digital or analog operation, but not both, and are limited by speed of actuation, range of pressure, time required for fabrication, or loss of power through pull-down resistors. Using the nonlinear mechanics intrinsic to structures composed of soft materials—in this case, by leveraging membrane inversion and tube kinking—two modular soft components are developed: a piston actuator and a bistable pneumatic switch. These two components combine to create valves capable of analog pressure regulation, simplified digital logic, controlled oscillation, nonvolatile memory storage, linear actuation, and interfacing with human users in both digital and analog formats. Three demonstrations showcase the capabilities of systems constructed from these valves: 1) a wearable glove capable of analog control of a soft artificial robotic hand based on input from a human user’s fingers, 2) a human-controlled cushion matrix designed for use in medical care, and 3) an untethered robot which travels a distance dynamically programmed at the time of operation to retrieve an object. This work illustrates pathways for complementary digital and analog control of soft robots using a unified valve design.

42 ENGINEERING↗
DOE OSTI2021

Species- and site-specific genome editing in complex bacterial communities

Knowledge of microbial gene functions comes from manipulating the DNA of individual strains in isolation from their natural communities. While this approach to microbial genetics has been foundational, its requirement for culturable microorganisms has left the majority of microbes and their interactions genetically unexplored. Here, we describe a generalizable strategy for editing the genomes of specific organisms within microbial communities. We identified genetically tractable bacteria within a community using Environmental Transformation Sequencing (ET-Seq), an approach in which non-targeted transposon integrations are mapped and quantified following community delivery. We next developed and used DNA-editing All-in-one RNA-guided CRISPR-Cas Transposase (DART) systems for targeted DNA insertion into organisms identified as tractable by ET-Seq, enabling organism- and locus-specific genetic manipulation within the community context. To illustrate the utility of our approach, we selectively edited closely related strains, measured gene fitness, and enriched targeted members within soil and infant gut microbiota. These results establish a new paradigm for targeted community editing relevant to research and applications on medical, agricultural, and industrial microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Temperature dependence of nickel ion release from nitinol medical devices

Abstract Nitinol exhibits unique (thermo)mechanical properties that make it central to the design of many medical devices. However, nitinol nominally contains 50 atomic percent nickel, which if released in sufficient quantities, can lead to adverse health effects. While nickel release from nitinol devices is typically characterized using in vitro immersion tests, these evaluations require lengthy time periods. We have explored elevated temperature as a potential method to expedite this testing. Nickel release was characterized in nitinol materials with surface oxide thickness ranging from 12 to 1564 nm at four different temperatures from 310 to 360 K. We found that for three of the materials with relatively thin oxide layers, ≤ 87 nm nickel release exhibited Arrhenius behavior over the entire temperature range with activation energies of 80 to 85 kJ/mol. Conversely, the fourth ''black–oxide'' material, with a much thicker, complex oxide layer, was not well characterized by an Arrhenius relationship. Power law release profiles were observed in all four materials; however, the exponent from the thin oxide materials was approximately 1/4 compared with 3/4 for the black–oxide material. To illustrate the potential benefit of using elevated temperature to abbreviate nickel release testing, we demonstrated that a > 50 day 310 K release profile could be accurately recovered by testing for less than 1 week at 340 K. However, because the materials explored in this study were limited, additional testing and mechanistic insight are needed to establish a protective temperature scaling that can be applied to all nitinol medical device components.

Saylor, David M.↗
DOE OSTI2020

Effects of deposition and annealing conditions on the crystallisation of NiTi thin films by e-beam evaporation

This Letter demonstrates the effects of deposition and annealing conditions on the crystallisation of nickel–titanium (NiTi) thin films deposited by the e-beam evaporation. NiTi thin films of 0.5 μm thick are fabricated on P-type (110) silicon substrates using the e-beam evaporation technique. We find that with the characterisations using the X-ray diffraction method, the results indicate that low e-beam deposition rate and argon employed annealing procedures can be beneficial to the formation of NiTi crystalline structure. Additionally, the as-deposited NiTi thin films without annealing procedures are amorphous, when deposited on the substrates at low temperatures. The differential scanning calorimetry studies show NiTi thin films formed by the e-beam evaporation are Ni-rich. Furthermore, SEM images illustrate that high-precision patterned NiTi thin film microstructures can be fabricated by combining with the e-beam evaporation and lift-off resist methods, without any wet or dry etching procedures. The proposed fabrication process for NiTi thin film microstructures shows great potential for application in shape memory alloy medical devices, as well as the micro-robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI

Deep Neural Networks (DNNs) can provide clinicians with fast and accurate predictions that are highly valuable for high-stakes medical decision-making, such as in brain tumor segmentation and treatment planning. However, these models largely lack transparency about the uncertainty in their predictions, potentially giving clinicians a false sense of reliability that may lead to grave consequences in patient care. Growing calls for Transparent and Responsible AI have promoted Uncertainty Quantification (UQ) to capture and communicate uncertainty in a systematic and principled manner. However, traditional Bayesian UQ methods remain prohibitively costly for large, million-dimensional tumor segmentation DNNs such as the U-Net. In this work, we discuss a computationally-efficient UQ approach via the partially Bayesian neural networks (pBNN). In pBNN, only a single layer, strategically selected based on gradient-based sensitivity analysis, is targeted for Bayesian inference. We illustrate the effectiveness of pBNN in capturing the full uncertainty for a 7.8-million parameter U-Net. We also demonstrate how practitioners and model developers can use the pBNN's predictions to better understand the model's capabilities and behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2022

Bacterial exometabolites influence Chlamydomonas cell cycle and double algal productivity

Algal-bacterial interactions provide clues to algal physiology, but mutualistic interactions are complicated by dynamic exchange. In this study, we characterized the response of Chlamydomonas reinhardtii to the presence of a putative alga-benefitting commensal bacterium ( Arthrobacter strain ‘P2b’). Co-cultivation promoted chlorophyll content, biomass, average cell size, and number of dividing cells, relative to axenic cultures. Addition of bacterial spent medium (whole, size-fractionated and heat-treated) had similar effects, indicating P2b does not require algal interaction to promote growth. Nutrients and pH were excluded as putative effectors, collectively indicating a commensal interaction mediated by Arthrobacter -released small exometabolite(s). Proteogenomic comparison revealed similar response to co-cultivation and spent media, including differential cell cycle regulation, extensive downregulation of flagellar genes and histones, carbonic anhydrase and RubisCO downregulation, upregulation of some chlorophyll, amino acid and carbohydrate biosynthesis genes, and changes to redox and Fe homeostasis. Further, Arthrobacter protein expression indicated some highly expressed putative secondary metabolites. Together, these results revealed that low molecular weight bacterial metabolites can elicit major physiological changes in algal cell cycle regulation, perhaps through a more productive G1 phase, that lead to substantial increases in photosynthetically-produced biomass. This work illustrates that model commensal interactions can be used to shed light on algal response to stimulating bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Arbuscular mycorrhiza convey significant plant carbon to a diverse hyphosphere microbial food web and mineral‐associated organic matter

Summary Arbuscular mycorrhizal fungi (AMF) transport substantial plant carbon (C) that serves as a substrate for soil organisms, a precursor of soil organic matter (SOM), and a driver of soil microbial dynamics. Using two‐chamber microcosms where an air gap isolated AMF from roots, we 13 CO 2 ‐labeled Avena barbata for 6 wk and measured the C Rhizophagus intraradices transferred to SOM and hyphosphere microorganisms. NanoSIMS imaging revealed hyphae and roots had similar 13 C enrichment. SOM density fractionation, 13 C NMR, and IRMS showed AMF transferred 0.77 mg C g −1 of soil (increasing total C by 2% relative to non‐mycorrhizal controls); 33% was found in occluded or mineral‐associated pools. In the AMF hyphosphere, there was no overall change in community diversity but 36 bacterial ASVs significantly changed in relative abundance. With stable isotope probing (SIP)‐enabled shotgun sequencing, we found taxa from the Solibacterales, Sphingobacteriales, Myxococcales, and Nitrososphaerales (ammonium oxidizing archaea) were highly enriched in AMF‐imported 13 C (> 20 atom%). Mapping sequences from 13 C‐SIP metagenomes to total ASVs showed at least 92 bacteria and archaea were significantly 13 C‐enriched. Our results illustrate the quantitative and ecological impact of hyphal C transport on the formation of potentially protective SOM pools and microbial roles in the AMF hyphosphere soil food web.

13C SIP↗
DOE OSTI2021

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

Neutrons have historically been used for a broad range of biological applications employing techniques such as small-angle neutron scattering, neutron spin echo, diffraction, and inelastic scattering. Unlike neutron scattering techniques that obtain information in reciprocal space, attenuation-based neutron imaging measures a signal in real space that is resolved on the order of tens of micrometers. The principle of neutron imaging follows the Beer-Lambert law and is based on the measurement of the bulk neutron attenuation through a sample. Greater attenuation is exhibited by some light elements (most notably, hydrogen), which are major components of biological samples. Contrast agents such as deuterium, gadolinium, or lithium compounds can be used to enhance contrast in a similar fashion as it is done in medical imaging, including techniques such as optical imaging, magnetic resonance imaging, X-ray, and positron emission tomography. For biological systems, neutron radiography and computed tomography have increasingly been used to investigate the complexity of the underground plant root network, its interaction with soils, and the dynamics of water flux in situ. Moreover, efforts to understand contrast details in animal samples, such as soft tissues and bones, have been explored. This manuscript focuses on the advances in neutron bioimaging such as sample preparation, instrumentation, data acquisition strategy, and data analysis using the High Flux Isotope Reactor CG-1D neutron imaging beamline. The aforementioned capabilities will be illustrated using a selection of examples in plant physiology (herbaceous plant/root/soil system) and biomedical applications (rat femur and mouse lung).

Bilheux, Hassina↗
DOE OSTI2022

IDAC-Bio, A Software for Internal Dosimetry Based on the New ICRP Biokinetic Models and Specific Absorbed Fractions

Radiation dosimetry is central to virtually all radiation safety applications, optimization, and research. It relates to various individuals and population groups and to miscellaneous exposure situations—including planned, existing, and emergency situations. The International Commission on Radiological Protection (ICRP) has developed a new computational framework for internal dose estimations. Important components are more detailed and improved anatomical models and more realistic biokinetic models than before. The ICRP is currently producing new organ dose and effective dose coefficients for occupational intakes of radionuclides (OIR) and environmental intakes of radionuclides (EIR), which supersede the earlier dose coefficients in Publication 68 and the Publication 72 series, respectively. However, the ICRP only publishes dose coefficients for a single acute intake of a radionuclide and for an integration period of 50 years for intake by adults and to age 70 years for intakes by pre-adults. The new software, IDAC-Bio, performs committed absorbed dose and effective dose calculations for a selectable intake scenario, e.g., for a continuous intake or an intake during x hours per day and y days per week, and for any selected integration time. The software uses the primary data and models of the ICRP biokinetic models and numerically solves the biokinetic model and calculates the absorbed doses to organs and tissues in the ICRP reference human phantoms. The software calculates absorbed dose using the nuclear decay data in ICRP publication 107. IDAC-Bio is a further development and an important addition to the internal dosimetry program IDAC-Dose2.1. Here, the results generated by the software were validated against published ICRP dose coefficients. The potential of the software is illustrated by dose calculations for a nuclear power plant worker who had been exposed to varying levels of 60 Co and who had undergone repeated whole-body measurements, and for a hypothetical member of the public subject to future releases of 148 Gd from neutron spallation in tungsten at the European Spallation Source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Overview of leakage scenarios in supervised machine learning

Machine learning (ML) provides powerful tools for predictive modeling. ML’s popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly implemented and evaluated, ML pipelines may contain leakage typically resulting in overoptimistic performance estimates and failure to generalize to new data. This can have severe negative financial and societal implications. Our aim is to expand understanding associated with causes leading to leakage when designing, implementing, and evaluating ML pipelines. Illustrated by concrete examples, we provide a comprehensive overview and discussion of various types of leakage that may arise in ML pipelines.

97 MATHEMATICS AND COMPUTING↗