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45 records · Page 3

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging

Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to about one millimeter in thickness. Typically, reducing fluorescence excitation exposure reduces the quality of the image. However, using deep learning super resolution techniques, these low-resolution images can be converted to high-resolution images. This work explores improving human tissue imaging by applying deep learning to maximize image quality while reducing fluorescence excitation exposure. We analyze two methods: a method based on U-Net, and a patch-based regression method. Both methods are evaluated on a skin dataset and an eye dataset. The eye dataset includes 1200 paired high power and low power images of retinal organoids. The skin dataset contains multiple frames of each sample of human skin. High-resolution images were formed by averaging 70 frames for each sample and low-resolution images were formed by averaging the first 7 and 15 frames for each sample. The skin dataset includes 550 images for each of the resolution levels. We track two measures of performance for the two methods: mean squared error (MSE) and structural similarity index measure (SSIM). For the eye dataset, the patches method achieves an average MSE of 27,611 compared to 146,855 for the U-Net method, and an average SSIM of 0.636 compared to 0.607 for the U-Net method. For the skin dataset, the patches method achieves an average MSE of 3.768 compared to 4.032 for the U-Net method, and an average SSIM of 0.824 compared to 0.783 for the U-Net method. Despite better performance on image quality, the patches method is worse than the U-Net method when comparing the speed of prediction, taking 303 seconds to predict one image compared to less than one second for the U-Net method.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Optical coherence tomography imaging of plant root growth in soil

Complex interactions between roots and soil provide the nutrients and physical support required for robust plant growth. Yet, visualizing the root-soil interface is challenged by soil’s opaque scattering characteristics. In this paper, we describe methods for using optical coherence tomography (OCT) to provide non-destructive 3D and cross-sectional root imaging not available with traditional bright field microscopy. OCT is regularly used for bioimaging, especially in ophthalmology where it can detect retinal abnormalities. Prior use of OCT in plant biology has focused on surface defects of above ground tissues, predominantly in food crops. Our results show OCT is also viable for detailed, in situ study of living plant roots.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Detection of large extracellular silver nanoparticle rings observed during mitosis using darkfield microscopy

During studies on the absorption and interactions between silver nanoparticles and mammalian cells grown in vitro it was observed that large extracellular rings of silver nanoparticles were deposited on the microscope slide, many located near post-mitotic cells. Silver nanoparticles (AgNP, 80nm), coated with citrate, were incubated at concentrations of 0.3 to 30 μg/ml with a human-derived culture of retinal pigment epithelial cells (ARPE-19) and observed using darkfield and fluorescent microscopy, 24 h after treatment. Approximately cell-sized extracellular rings of deposited AgNP were observed on the slides among a field of dispersed individual AgNP. The mean diameter of 45 nanoparticles circles was 62.5 +/-12 microns. Ring structures were frequently observed near what appeared to be post-mitotic daughter cells, giving rise to the possibility that cell membrane fragments were deposited on the slide during mitosis, and those fragments selectively attracted and retained silver nanoparticles from suspension in the cell culture medium. These circular structures were observable for the following technical reasons: 1) darkfield microscope could observe single nanoparticles below 100 nm in size, 2) a large concentration (10 8 and 10 9 ) of nanoparticles was used in these experiments 3) negatively charged nanoparticles were attracted to adhesion membrane proteins remaining on the slide from mitosis. The observation of silver nanoparticles attracted to apparent remnants of cellular mitosis could be a useful tool for the study of normal and abnormal mitosis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Biometric Access Control System Industrialization

The Biometric Access Control System Industrialization project was initiated as Project 2 under the umbrella Cooperative Research and Development Agreement (CRADA) No. SC 14/01816.00.00 between National Technology and Engineering Solutions of Sandia (NTESS) and AQUILA on July 16, 2019. The purpose of this project has been to evaluate alternatives to the more traditional biometric access control methods, such as fingerprints and retinal scanners.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Light: An Extrinsic Factor Influencing Animal-based Research

Abstract Light is an environmental factor that is extrinsic to animals themselves and that exerts a profound influence on the regulation of circadian, neurohormonal, metabolic, and neurobehavioral systems of all animals, including research animals. These widespread biologic effects of light are mediated by distinct photoreceptors—rods and cones that comprise the conventional visual system and melanopsin-containing intrinsically photosensitive retinal ganglion cells (ipRGCs) of the nonvisual system that interact with the rods and cones. The rods and cones of the visual system, along with the ipRGCs of the nonvisual system, are species distinct in terms of opsins and opsin concentrations and interact with one another to provide vision and regulate circadian rhythms of neurohormonal and neurobehavioral responses to light. Here, we review a brief history of lighting technologies, the nature of light and circadian rhythms, our present understanding of mammalian photoreception, and current industry practices and standards. We also consider the implications of light for vivarium measurement, production, and technological application and provide simple recommendations on artificial lighting for use by regulatory authorities, lighting manufacturers, designers, engineers, researchers, and research animal care staff that ensure best practices for optimizing animal health and well-being and, ultimately, improving scientific outcomes.

Veterinary Sciences↗

DOE OSTI2022

An EvoDevo Study of Salmonid Visual Opsin Dynamics and Photopigment Spectral Sensitivity

Salmonids are ideal models as many species follow a distinct developmental program from demersal eggs and a large yolk sac to hatching at an advanced developmental stage. Further, these economically important teleosts inhabit both marine- and freshwaters and experience diverse light environments during their life histories. At a genome level, salmonids have undergone a salmonid-specific fourth whole genome duplication event (Ss4R) compared to other teleosts that are already more genetically diverse compared to many non-teleost vertebrates. Thus, salmonids display phenotypically plastic visual systems that appear to be closely related to their anadromous migration patterns. This is most likely due to a complex interplay between their larger, more gene-rich genomes and broad spectrally enriched habitats; however, the molecular basis and functional consequences for such diversity is not fully understood. This study used advances in genome sequencing to identify the repertoire and genome organization of visual opsin genes (those primarily expressed in retinal photoreceptors) from six different salmonids [Atlantic salmon ( Salmo salar ), brown trout ( Salmo trutta ), Chinook salmon ( Oncorhynchus tshawytcha ), coho salmon ( Oncorhynchus kisutch ), rainbow trout ( Oncorhynchus mykiss ), and sockeye salmon ( Oncorhynchus nerka )] compared to the northern pike ( Esox lucius ), a closely related non-salmonid species. Results identified multiple orthologues for all five visual opsin classes, except for presence of a single short-wavelength-sensitive-2 opsin gene. Several visual opsin genes were not retained after the Ss4R duplication event, which is consistent with the concept of salmonid rediploidization. Developmentally, transcriptomic analyzes of Atlantic salmon revealed differential expression within each opsin class, with two of the long-wavelength-sensitive opsins not being expressed before first feeding. Also, early opsin expression in the retina was located centrally, expanding dorsally and ventrally as eye development progressed, with rod opsin being the dominant visual opsin post-hatching. Modeling by spectral tuning analysis and atomistic molecular simulation, predicted the greatest variation in the spectral peak of absorbance to be within the Rh2 class, with a ∼40 nm difference in λ max values between the four medium-wavelength-sensitive photopigments. Overall, it appears that opsin duplication and expression, and their respective spectral tuning profiles, evolved to maximize specialist color vision throughout an anadromous lifecycle, with some visual opsin genes being lost to tailor marine-based vision.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Development of Novel Small-Molecule Activators of Pyruvate Kinase Muscle Isozyme 2, PKM2, to Reduce Photoreceptor Apoptosis

Treatment options are lacking to prevent photoreceptor death and subsequent vision loss. Previously, we demonstrated that reprogramming metabolism via the pharmacologic activation of PKM2 is a novel photoreceptor neuroprotective strategy. However, the features of the tool compound used in those studies, ML-265, preclude its advancement as an intraocular, clinical candidate. This study sought to develop the next generation of small-molecule PKM2 activators, aimed specifically for delivery into the eye. Compounds were developed that replaced the thienopyrrolopyridazinone core of ML-265 and modified the aniline and methyl sulfoxide functional groups. Compound 2 demonstrated that structural changes to the ML-265 scaffold are tolerated from a potency and efficacy standpoint, allow for a similar binding mode to the target, and circumvent apoptosis in models of outer retinal stress. To overcome the low solubility and problematic functional groups of ML-265, compound 2’s efficacious and versatile core structure for the incorporation of diverse functional groups was then utilized to develop novel PKM2 activators with improved solubility, lack of structural alerts, and retained potency. No other molecules are in the pharmaceutical pipeline for the metabolic reprogramming of photoreceptors. Thus, this study is the first to cultivate the next generation of novel, structurally diverse, small-molecule PKM2 activators for delivery into the eye.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Insights into pathological mechanisms and interventions revealed by analyzing a mathematical model for cone metabolism

This work analyzes a mathematical model for the metabolic dynamics of a cone photoreceptor, which is the first model to account for energy generation from fatty acids oxidation of shed photoreceptor outer segments (POS). Multiple parameter bifurcation analysis shows that joint variations in external glucose, the efficiency of glucose transporter 1 (GLUT1), lipid utilization for POS renewal, and oxidation of fatty acids affect the cone’s metabolic vitality and its capability to adapt under glucose-deficient conditions. The analysis further reveals that when glucose is scarce, cone viability cannot be sustained by only fueling energy production in the mitochondria, but it also requires supporting anabolic processes to create lipids necessary for cell maintenance and repair. In silico experiments are used to investigate how the duration of glucose deprivation impacts the cell without and with a potential GLUT1 or oxidation of fatty acids intervention as well as a dual intervention. The results show that for prolonged duration of glucose deprivation, the cone metabolic system does not recover with higher oxidation of fatty acids and requires greater effectiveness of GLUT1 to recover. Finally, time-varying global sensitivity analysis (GSA) is applied to assess the sensitivity of the model outputs of interest to changes and uncertainty in the parameters at specific times. The results reveal a critical temporal window where there would be more flexibility for interventions to rescue a cone cell from the detrimental consequences of glucose shortage.

59 BASIC BIOLOGICAL SCIENCES↗