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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Rodent Research on the International Space Station - A Look Forward

Rodent Research on the International Space Station (ISS) is one of the highest priority science activities being supported by NASA and is planned for up to two flights per year. The first Rodent Research flight, Rodent Research-1 (RR-1) validates the hardware and basic science operations (dissections and tissue preservation). Subsequent flights will add new capabilities to support rodent research on the ISS. RR-1 will validate the following capabilities: animal husbandry for up to 30 days, video downlink to support animal health checks and scientific analysis, on-orbit dissections, sample preservation in RNA. Later and formalin, sample transfer from formalin to ethanol (hindlimbs), rapid cool-down and subsequent freezing at -80 of tissues and carcasses, sample return and recovery. RR-2, scheduled for SpX-6 (Winter 20142015) will add the following capabilities: animal husbandry for up to 60 days, RFID chip reader for individual animal identification, water refill and food replenishment, anesthesia and recovery, bone densitometry, blood collection (via cardiac puncture), blood separation via centrifugation, soft tissue fixation in formalin with transfer to ethanol, and delivery of injectable drugs that require frozen storage prior to use. Additional capabilities are also planned for future flights and these include but are not limited to male mice, live animal return, and the development of experiment unique equipment to support science requirements for principal investigators that are selected for flight. In addition to the hardware capabilities to support rodent research the Crew Office has implemented a training program in generic rodent skills for all USOS crew members during their pre-assignment training rotation. This class includes training in general animal handling, euthanasia, injections, and dissections. The dissection portion of this training focuses on the dissection of the spleen, liver, kidney with adrenals, brain, eyes, and hindlimbs. By achieving and maintaining proficiency in these basic skills as part of the nominal astronaut training curriculum this allows the rodent research program to focus the mission specific crew training on scientific requirements of research and operations flow.

Experimental capabilities↗
NASA NTRS1986

Program for Experimentation With Expert Systems

CERBERUS is forward-chaining, knowledge-based system program useful for experimentation with expert systems. Inference-engine mechanism performs deductions according to user-supplied rule set. Information stored in intermediate area, and user interrogated only when no applicable data found in storage. Each assertion posed by CERBERUS answered with certainty ranging from 0 to 100 percent. Rule processor stops investigating applicable rules when goal reaches certainty of 95 percent or higher. Capable of operating for wide variety of domains. Sample rule files included for animal identification, pixel classification in image processing, and rudimentary car repair for novice mechanic. User supplies set of end goals or actions. System complexity decided by user's rule file. CERBERUS written in FORTRAN 77.

Engle, S. W.↗
DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗
DOE OSTI2021

Do Remote Camera Arrangements and Image Capture Settings Improve Individual Identification of Golden Eagles?

Individual identification of animals from camera traps has become an important task in wildlife research, but camera deployment methods often do not facilitate this important undertaking. Identification of individual golden eagles (Aquila chrysaetos) is possible using uniquely marked rectrices, but no studies have explored methods to maximize the rate of individual identification from camera images. Furthermore, our objectives were to assess whether different camera heights (1 m vs. 3 m), image capture settings (one image after a 1-min delay vs. burst of 5 images after a 30 sec delay), and arrangements relative to bait (dorsally vs. ventrally aimed) affected views of rectrices on golden eagles and our ability to identify individuals. We conducted our study from 15 December 2016 to 3 March 2017 on the Savannah River Site, South Carolina. First, we developed a scoring system based on views of rectrices and used a linear mixed-effects model to compare image scores among different camera arrangements and image settings. Next, after identifying individual eagles, we used generalized linear mixed-effects models to compare total individual eagle detections, total days an individual was detected, and probability of obtaining an unknown individual identification among camera arrangements and settings. Overall, we scored a total of 27,499 images, with 8,083 providing views of marked rectrices that allowed identification of 18 individual eagles. Average image scores and proportion of images suitable for individual identification were higher from elevated (3 m) camera arrangements than standard arrangements (1 m) across sites. Regardless of camera height, faster frequency of image capture provided more images that could be used to identify individuals and the most trap days per individual. Researchers and managers should consider deploying elevated cameras traps with faster frequency of image capture to improve data quality and potential for analysis of golden eagle populations and trends across the species’ range.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

System identification of perilymphatic fistula in an animal model

An acute animal model has been developed in the chinchilla for the study of perilymphatic fistulas. Micropunctures were made in three sites to simulate bony, round window, and oval window fistulas. The eye movements in response to pressure applied to the external auditory canal were recorded after micropuncture induction and in preoperative controls. The main pressure stimulus was a pseudorandom binary sequence (PRBS) that rapidly changed between plus and minus 200 mm of water. The PRBS stimulus, with its wide frequency bandwidth, produced responses clearly above the preoperative baseline in 78 percent of the runs. The response was better between 0.5 and 3.3 Hz than it was below 0.5 Hz. The direction of horizontal eye movement was toward the side of the fistula with positive pressure applied in 92 percent of the runs. Vertical eye movements were also observed. The ratio of vertical eye displacement to horizontal eye displacement depended upon the site of the micropuncture induction. Thus, such a ratio measurement may be clinically useful in the noninvasive localization of perilymphatic fistulas in humans.

Non-NASA Center↗
DOE OSTI2022

Technical Performance and Cost Optimization of Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) for Wide-area Surveillance and Identification of Marine Life at Marine Energy Installations

Florida Atlantic University developed an underwater optical monitoring system prototype - Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) - suitable for marine energy full project lifecycle observation (baseline, commissioning, and decommissioning), with an automated real-time classification of marine animals. With precursor 2014 DOE funding (award DE-EE0006787), a prototype UMSLI was demonstrated in a controlled laboratory environment and achieved a TRL 6. This EERE DE-EE0007828 award aimed to both increase the TRL of the UMSLI by improving the technology performance (e.g., increase distance of marine animal target detection capability, add additional species classification capabilities, improve the system performance during the day, etc.) and reduce the cost. The UMSLI presents a novel application of underwater distributed Light Detection And Ranging (LiDAR), an advanced remote sensing method that uses light in the form of laser pulses for an application, this paired with an algorithm provides 360 degrees underwater detection, imaging, and classification of marine life. This solution for underwater monitoring of biota preserves the advantages of traditional optical and acoustic solutions while overcoming many associated disadvantages for marine energy site environmental monitoring, such as difficulties in species detection and classification in low light or night and turbid environments. This new approach is a purposefully designed, reconfigurable adaptation of an existing class 3B laser technology into one UMSLI instrument that can be easily mounted on or around different classes of marine energy equipment, such as devices to capture ocean current energy or wave energy. The system uses relatively low average power, utilizes far-red (> 635nm) laser illumination to be invisible and eye-safe to marine animals, is compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations (i.e., current design is targeting more than 7 days continuous operation), to inherently generate a sparse primary dataset that only includes detected anomalies, and to allow robust real-time automated animal classification and identification with a low data bandwidth requirement. The technology’s overarching goal for application, is a system that can be deployed to collect pre-installation baseline species observations at a proposed marine energy deployment site with minimal post-processing overhead. The envisioned system will also produce high-resolution imagery of marine animals through a wide range of conditions and support automated tracking and notification of the presence of managed animals within established perimeters of marine energy equipment to satisfy deployed marine energy projects’ endangered and threatened species monitoring requirements. Through the current project, we demonstrated the UMSLI prototype in an operational environment and increased the UMSLI’s Technology Readiness Level from 6 to 7. The project resulted in many novel technologies, including: 1) an eye-safe red laser based, low-cost LiDAR system that can detect targets up to 10 meters distance; 2) the GAN-based machine learning underwater LiDAR image enhancement technique (this is the first known application of GAN technique in underwater LiDAR); 3) LiDAR-based real-time automated detection capabilities; and 4) a template matching based automated classification tool. These technologies build a solid foundation for future efforts to develop an extended range electro-optical monitoring system suitable for marine energy deployments. In addition to technology development, a key lesson learned is that addressing regulatory and safety requirements must be front and center in any marine energy monitoring applications.

16 TIDAL AND WAVE POWER↗
NASA NTRS1984

NASA space biology accomplishments, 1983-84

Approximately 42 project summaries from NASA's Space Biology Program are presented. Emphasis is placed on gravitational effects on plant and animal life. The identification of gravity perception; the effects of weightlessness on genetic integrity, cellular differentiation, reproduction, development, growth, maturation, and senescence; and how gravity affects and controls physiology, morphology, and behavior of organisms are studied.

Halstead, T. W.↗
DOE OSTI2020

Swine acute diarrhea syndrome coronavirus replication in primary human cells reveals potential susceptibility to infection

Zoonotic coronaviruses represent an ongoing threat, yet the myriads of circulating animal viruses complicate the identification of higher-risk isolates that threaten human health. Swine acute diarrhea syndrome coronavirus (SADS-CoV) is a newly discovered, highly pathogenic virus that likely evolved from closely related HKU2 bat coronaviruses, circulating in Rhinolophus spp. bats in China and elsewhere. As coronaviruses cause severe economic losses in the pork industry and swine are key intermediate hosts of human disease outbreaks, we synthetically resurrected a recombinant virus (rSADS-CoV) as well as a derivative encoding tomato red fluorescent protein (tRFP) in place of ORF3. rSADS-CoV replicated efficiently in a variety of continuous animal and primate cell lines, including human liver and rectal carcinoma cell lines. Of concern, rSADS-CoV also replicated efficiently in several different primary human lung cell types, as well as primary human intestinal cells. rSADS-CoV did not use human coronavirus ACE-2, DPP4, or CD13 receptors for docking and entry. Contemporary human donor sera neutralized the group I human coronavirus NL63, but not rSADS-CoV, suggesting limited human group I coronavirus cross protective herd immunity. Importantly, remdesivir, a broad-spectrum nucleoside analog that is effective against other group 1 and 2 coronaviruses, efficiently blocked rSADS-CoV replication in vitro. rSADS-CoV demonstrated little, if any, replicative capacity in either immune-competent or immunodeficient mice, indicating a critical need for improved animal models. Efficient growth in primary human lung and intestinal cells implicate SADS-CoV as a potential higher-risk emerging coronavirus pathogen that could negatively impact the global economy and human health.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1978

Tissue identification by ultrasound

The ultrasonic properties of animal and human soft tissue were measured over the frequency range of 1.5 to 10.0 MHz. The method employed a swept-frequency, coherent technique known as time delay spectrometry. Measurements of attenuation versus frequency on liver, backfat, kidney, pancreas, spleen, breast, and other tissue were made. Considerable attention was paid to tissue handling and in determining the effects of fixing on the attenuation of ultrasound in the tissue.

Lecroissette, D. H.↗
DOE OSTI2021

Deletion of the A137R Gene from the Pandemic Strain of African Swine Fever Virus Attenuates the Strain and Offers Protection against the Virulent Pandemic Virus

African swine fever virus (ASFV) is causing a devastating pandemic in domestic and wild swine within an extended geographical area from Central Europe to East Asia, resulting in economic losses for the regional swine industry. There are no commercial vaccines; therefore, disease control relies on identification and culling of infected animals. We report here that the deletion of the ASFV gene A137R from the highly virulent ASFV-Georgia2010 (ASFV-G) isolate induces a significant attenuation of virus virulence in swine. A recombinant virus lacking the A137R gene, ASFV-G-DA137R, was developed to assess the role of this gene in ASFV virulence in domestic swine. Animals inoculated intramuscularly with 102 50% hemadsorption doses (HAD50) of ASFV-G-DA137R remained clinically healthy during the 28-day observational period. All animals inoculated with ASFV-G-DA137R had medium to high viremia titers and developed a strong virus-specific antibody response. Importantly, all ASFV-G-DA137Rinoculated animals were protected when challenged with the virulent parental strain ASFV-G. No evidence of replication of challenge virus was observed in the ASFV-GDA137R-inoculated animals. Therefore, ASFV-G-DA137R is a novel potential live attenuated vaccine candidate and one of the few experimental vaccine strains reported to induce protection against the highly virulent ASFV Georgia virus that is the cause of the current Eurasian pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

collisional cross section↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35370-DND

The above anvil cirrus plume (AACP) is a weather phenomenon that signifies an intense tropopause penetrating updraft which can inject cirrus clouds several kilometers into the stratosphere. Storms that have such intense updrafts are often supercells which generate severe weather such as tornadoes, high winds, and hail. In addition, AACPs moisten the stratosphere and influence the Earth's radiative balance. Though an AACP can be identified by the human eye, no automated AACP detection methods currently exist. Lack of detection inhibits understanding of where and how often AACPs occur, and how these storms influence stratospheric air composition. Previous work involved synthesis of multiple remote sensing and severe weather report/warning data sources to identify AACPs in Geostationary Operational Environmental Satellite system (GOES) satellite imagery and better understand their weather impacts (Bedka et al. (Wea. Forecasting, 2018)). This current study demonstrates an automated AACP identification method based on the application of a deep learning segmentation model known as a U-net. This study documents the development of a U-net model capable of identifying emergent AACPs using only satellite infrared (IR) and visible reflected sunlight imagery. The performance of a U-net is quantitatively benchmarked with human AACP identifications and qualitatively assessed through animations of detections generated from GOES-16 1-minute temporal resolution imagery.

Charles Liles↗
NASA NTRS2021

Topical: Reverse Translation Strategies to Support Cognitive and Behavioral Risk Characterization

A coordinated suite of measurements to assess humans and animals needs to be established to support the translation and harmonization of animal research data to the astronaut corps due to the necessity of using animal subjects for radiation testing. The identification of POLs and PELs for spaceflight stressors (i.e. space radiation, altered gravity, isolation and confinement, sleep disruption) individually and combined will depend on defining scaling factors or transfer functions that can be used to relate human and animal outcomes.

Gregory Nelson↗
DOE OSTI2023

Occurrence and molecular characterization of Enterocytozoon bieneusi in wild and domestic animal species in Portugal

Abstract The phylum Microsporidia encompasses a diverse group of obligate, intracellular, and spore-forming organisms able to infect a wide range of animal hosts. Among them, Enterocytozoon bieneusi is the most frequently reported species in humans and animals. Little is known about the presence and epidemiology of E. bieneusi in wildlife. We investigated E. bieneusi occurrence and genetic diversity in wild and domestic mammals, through molecular-detection methods, from different regions across Portugal. A total of 756 samples were collected from 288, 242, and 226 wild carnivores, wild ungulates, and domestic animals, respectively. Overall, eight specimens were E. bieneusi-positive (1.1%, 8/756) obtained from five wild (Iberian lynx, Iberian wolf, red fox, stone marten, and wild boar) and one domestic (sheep) host. Nucleotide sequence analysis identified four genotypes of E. bieneusi, Type IV, Wildboar3, BEB6, and PtEbIX. Three of those genotypes belong to Groups 1 (Type IV and Wildboar3) and 2 (BEB6), which are known to contain genotypes capable of infecting a variety of hosts, including humans, highlighting their public health importance. PtEbIX belongs to the dog-specific Group 11. This study represents the first, largest, and most comprehensive molecular-based epidemiology survey carried out in Portugal in wild and domestic animals to date and the first worldwide identification of E. bieneusi in wolf species. Our study showed that wild carnivores and ungulates may act as reservoirs of zoonotic genotypes of E. bieneusi, establishing their role in maintaining the sylvatic cycle of this parasite while representing a potential source of infection for humans and domestic animals.

Figueiredo, Ana M. (ORCID:0000000226236340)↗
DOE OSTI2021

A miniaturized long-life acoustic transmitter for animal tracking in marine environments

Marine and hydrokinetic (MHK) energy devices are early-stage technologies and their impacts to ocean life and environment are largely unknown, which consequently limits the progress of deploying these devices at commercial scale. Optical and acoustic imaging have been the primary technologies for monitoring marine animals’ behavior at existing or potential MHK sites. However, these technologies lack the ability to identify individual animals, which makes them less useful in identifying protected species as well as studying the animals’ long-term behavior. Implantable acoustic transmitters with unique identification codes could be used as a complementary technology. To minimize the tag burden on the tagged animal (and hence the resulting bias) and to satisfy the long-term monitoring needs, it is desired that the implanted transmitter be as small and light as possible and can last for as long as possible. To date, the smallest acoustic transmitters suitable for marine animal tracking have relatively short service life and detection range (up to 200 m). In this report, we introduce a new miniaturized acoustic transmitter which operates at 208.3 kHz. It offers a smaller dry weight (0.45 g), a significantly longer service life (140 days at a 5-second ping rate) as well as a greater detection range than the existing commercial counterparts, which will help obtain more comprehensive movement and behavioral information of animals around MHK energy devices.

Li, Huidong↗