Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Disease ecology”

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

The Epidemiology of Coccidioidomycosis (Valley fever) and the Disease Ecology of Coccidioides spp. in New Mexico (2006–2023)

Coccidioidomycosis (Valley fever), caused by Coccidioides spp., is a fungal infection endemic to semi-arid regions of the Americas. Despite 80 years of disease recognition in New Mexico, there is limited disease awareness. We incorporated clinical, epidemiological, and ecological datasets to summarize the knowledge of Valley fever in New Mexico. We analyzed 1541 human cases from 2006 to 2023. On average, 86 cases were reported each year (4.1 cases per 100,000 population per year). The highest levels of incidence were in southwestern New Mexico. American Indian or Alaska Natives in New Mexico had a 1.9 times higher incidence rate of coccidioidomycosis than White people, and among age groups, older populations in New Mexico had the highest incidence rates. We analyzed 300 soil samples near Las Cruces, New Mexico, for the presence of Coccidioides and reported the first known positive soil samples collected from the state, the majority of which were from grassland-dominated sites and from animal burrows. Sequence analyses in clinical specimens, wild animals, and soil samples confirmed that Coccidioides posadasii is the main causative species of coccidioidomycosis in New Mexico. Environmental surveillance validated that locally acquired infections could occur in, but are not limited to, Catron, Doña Ana, Sierra, and Socorro Counties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Transient disease dynamics across ecological scales

Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Parabacteroides distasonis : intriguing aerotolerant gut anaerobe with emerging antimicrobial resistance and pathogenic and probiotic roles in human health

Parabacteroides distasonis is the type strain for the genus Parabacteroides, a group of gram-negative anaerobic bacteria that commonly colonize the gastrointestinal tract of numerous species. First isolated in the 1930s from a clinical specimen as Bacteroides distasonis, the strain was re-classified to form the new genus Parabacteroides in 2006. Currently, the genus consists of 15 species, 10 of which are listed as 'validly named' (P. acidifaciens, P. chartae, P. chinchillae, P. chongii, P. distasonis, P. faecis, P. goldsteinii, P. gordonii, P. johnsonii, and P. merdae) and 5 'not validly named' (P. bouchesdurhonensis, P. massiliensis, P. pacaensis, P. provencensis, and P. timonensis) by the List of Prokaryotic names with Standing in Nomenclature. The Parabacteroides genus has been associated with reports of both beneficial and pathogenic effects in human health. Herein, we review the literature on the history, ecology, diseases, antimicrobial resistance, and genetics of this bacterium, illustrating the effects of P. distasonis on human and animal health.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Linking Individual Variation in Movement Behavior to Infection Dynamics in Highly Mobile Species

Animal movements are linked to infectious disease dynamics in wildlife through multiple mechanisms across various spatial and temporal scales. For example, infections can impede the movement of infected individuals, but infected hosts can also disperse infectious agents as they move. Individuals inevitably vary in their responses to infections and their roles in determining infection dynamics, which means that considering variation among individuals – whether across species, across time, or within populations – is important for understanding movement-disease relationships and their implications for epidemiology. Here, I draw on studies from multiple systems to examine how variation in movement behavior links to infectious disease epidemiology. For example, a comparative study of habitat selection across multiple waterfowl species highlights that diel behavioral patterns, rather than individual or seasonal variation, is the most important driver of the risk of overlap between waterfowl and poultry farms, and therefore for the risk of avian influenza spillover. Together, these studies show that individual traits interact with the environment to influence the roles of individuals for infectious disease ecology.

Claire Stewart Teitelbaum↗
NASA NTRS1986

The role of plant disease in the development of controlled ecological life support systems

Plant diseases could be important factors affecting growth of higher plants in Closed Ecological Life Support Systems (CELSS). Disease control, therefore, will be needed to maintain healthy plants. The most important controls should be aimed at preventing the introduction, reproduction and spread of pathogens and preventing plant infection. An integrared ease control program will maximize that approach. In the design and operation of CELSS, plant disease should be considered an important aspect of plant growth. The effects of plant diseases are reviewed and several disease control measures are discussed.

Nelson, B.↗
DOE OSTI2024

Exploring Climate-Disease Connections in Geopolitical Versus Ecological Regions: The Case of West Nile Virus in the United States

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Raccoon densities across four land cover types in the southeastern United States

Raccoons (Procyon lotor) are the primary reservoir for rabies virus in eastern North America. Management of rabies in raccoons is achieved primarily with the use of oral rabies vaccination (ORV) and effective ORV bait densities are determined in part by the densities of raccoons. Decisions regarding ORV bait densities, however, are limited by an incomplete understanding of raccoon densities across the spectrum of landscapes they occupy. We carried out a mark-recapture study of raccoons on the Savannah River Site in South Carolina, USA, from 2017–2019, to develop sex- and landscape-specific raccoon density estimates across 4 rural land cover types in the southeastern United States: bottomland hardwood, riparian forest, isolated wetland, and upland pine (Pinus spp.). We captured 404 unique raccoons 773 times over the 3-year trapping period. Estimated densities were 5.44 ± 0.37 (SE) animals/km 2 in bottomland hardwood forest, 2.62 ± 0.32 animals/km 2 in riparian forest, 2.19 ± 0.29 animals/km 2 in isolated wetlands, and 2.14 ± 0.23 animals/km 2 in upland pine. Densities were significantly higher in bottomland hardwood than all other land cover types, whereas densities among the remaining cover types were similar. These patterns are likely the result of landscape fragmentation and configuration, with riparian forests typically embedded in a matrix of less suitable cover types, leading to low densities despite presumably high resource availability. There were higher densities of males than females in every cover type except upland pine, where the sex ratio was balanced. Densities on our site were low compared to other rural areas, which likely results from the lack of human influence in terms of agriculture or development. The financial cost of baiting for ORV distribution may be reduced by considering the comparatively low densities of raccoons in these rural landscapes in the southeastern United States.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Racoons ( Procyon Lotor ) Show Higher Trypanosoma Cruzi Detection Rates than Virginia Opossums ( Didelphis Virginiana ) in South Carolina, USA

Chagas disease, a significant public health concern in the Americas, is caused by a protozoan parasite, Trypanosoma cruzi. The life cycle of T. cruzi involves kissing bugs (Triatoma spp.) functioning as vectors and mammalian species serving as hosts. Raccoons (Procyon lotor) and opossums (Didelphis virginiana) have been identified as important reservoir species in the life cycle of T. cruzi, but prevalence in both species in the southeastern US is currently understudied. We quantified T. cruzi prevalence in these two key reservoir species across our study area in South Carolina, US, and identified factors that may influence parasite detection. We collected whole blood from 183 raccoons and 126 opossums and used PCR to detect the presence of T. cruzi. We then used generalized linear models with parasite detection status as a binary response variable and predictor variables of land cover, distance to water, sex, season, and species. Our analysis indicated that raccoons experienced significantly higher parasite detection rates than Virginia opossums, with T. cruzi prevalence found to be 26.5% (95% confidence interval [CI], 20.0–33.8) in raccoons and 10.5% (95% CI, 5.51–17.5) in opossums. Altogether, our results concur with previous studies, in that T. cruzi is established in reservoir host populations in natural areas of the southeastern US.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

A Thermodynamic Paradigm for Studying Disease Vector’s Habitats & Life Cycles Using NASA’s NextGen Remote Sensing Instruments

Global public health is entering a new informational age through the use of spatial models of disease vector/host ecologies driven by the use of remotely sensed data to measure environmental and structural factors critical in determining disease vector habitats, distributions, life cycles, and host interactions. The vector habitat microclimates can be quantified in terms of the surface energy budget measured by satellites. The epidemiological equations (processes) can be adapted and modified to explicitly incorporate environmental factors and interfaces required by a specific disease and its vector/host cycle. Remote sensing can be used to measure or evaluate or estimate both environment (state functions) and interface (process functions). Remote sensing can be used to measure or evaluate or estimate both environment (state functions) and interface (process functions) defining vector habitats. The products of remote sensing can be integrated directly into the epidemiological equations to significantly enhance our understanding of disease vector’s life cycles and habitats. The next generation of NASA’s remote sensing instruments recently operational will provide a significant enhancement in our ability to study disease vector’s life cycles and habitats. Three new instruments have become operational on the International Space Station (ISS) In June 2018, ECOSTRESS (The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), a five channel, thermal IR instrument with 70 m resolution and approximately 4-5 day repeat cycle of day/night pairs along with DESISa hyperspectral sensor system 235 channels (between 400 and 1000 nanometers), 30 m resolution, and data only being acquired on demand. In November 2018. GEDI (Global Ecosystem Dynamics Investigation). GEDI is a high-resolution laser ranging of Earth’s forests and topography. These instruments set the stage for NASA’s new global designated observables set of remote sensing measurements collecting hyperspectral and multispectral thermal data called SBG (Surface Biology Geology). The SBG is currently in the planning stage. The availability and use of level 1-4 data products generated from these ISS instruments will be discussed.

thermodynamics↗
DOE OSTI2021

Comparative genome analyses suggest a hemibiotrophic lifestyle and virulence differences for the beech bark disease fungal pathogens Neonectria faginata and Neonectria coccinea

Abstract Neonectria faginata and Neonectria coccinea are the causal agents of the insect-fungus disease complex known as beech bark disease (BBD), known to cause mortality in beech forest stands in North America and Europe. These fungal species have been the focus of extensive ecological and disease management studies, yet less progress has been made toward generating genomic resources for both micro- and macro-evolutionary studies. Here, we report a 42.1 and 42.7 mb highly contiguous genome assemblies of N. faginata and N. coccinea, respectively, obtained using Illumina technology. These species share similar gene number counts (12,941 and 12,991) and percentages of predicted genes with assigned functional categories (64 and 65%). Approximately 32% of the predicted proteomes of both species are homologous to proteins involved in pathogenicity, yet N. coccinea shows a higher number of predicted mitogen-activated protein kinase genes, virulence determinants possibly contributing to differences in disease severity between N. faginata and N. coccinea. A wide range of genes encoding for carbohydrate-active enzymes capable of degradation of complex plant polysaccharides and a small number of predicted secretory effector proteins, secondary metabolite biosynthesis clusters and cytochrome oxidase P450 genes were also found. This arsenal of enzymes and effectors correlates with, and reflects, the hemibiotrophic lifestyle of these two fungal pathogens. Phylogenomic analysis and timetree estimations indicated that the N. faginata and N. coccinea species divergence may have occurred at ∼4.1 million years ago. Differences were also observed in the annotated mitochondrial genomes as they were found to be 81.7 kb (N. faginata) and 43.2 kb (N. coccinea) in size. The mitochondrial DNA expansion observed in N. faginata is attributed to the invasion of introns into diverse intra- and intergenic locations. These first draft genomes of N. faginata and N. coccinea serve as valuable tools to increase our understanding of basic genetics, evolutionary mechanisms and molecular physiology of these two nectriaceous plant pathogenic species.

Salgado-Salazar, Catalina↗
DOE OSTI2026

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Assessment of habitat‐specific competition for oral rabies vaccine baits between raccoons and opossums

Abstract Throughout the eastern United States, the National Rabies Management Program (NRMP) distributes oral rabies vaccine (ORV) baits to manage rabies virus circulation in raccoon ( Procyon lotor ) populations. The consumption of vaccine baits by non‐target species including Virginia opossums ( Didelphis virginiana ) may reduce the effectiveness of ORV programs, but competition for baits remains poorly quantified in many areas of the southeastern United States. We distributed placebo ORV baits injected with a biomarker across 4 land cover types (bottomland hardwood, upland pine, riparian, isolated wetland) on the Savannah River Site in South Carolina, USA, 2017–2019. We then trapped and collected whiskers from 247 raccoons and 78 opossums to assess biomarker presence using fluorescent microscopy. Our data revealed greater bait uptake probability by raccoons (estimated x̅ = 0.30, 95% CI = 0.19–0.44) compared to opossums (estimated x̅ = 0.11, 95% CI = 0.05–0.23) across all cover types surveyed. Probability of bait consumption was not affected by cover type or the abundance of raccoons or opossums. Among raccoons, males were more likely to consume baits than females (estimated x̅ = 0.28, 95% CI = 0.17–0.44 for males and 0.14, 95% CI = 0.05–0.31 for females) and probability of consumption increased by 0.08 with each additional day trapped during the 10‐day trapping session. Uptake rates for raccoons were relatively low compared to other studies and not influenced by competition with opossums. These low consumption rates indicate that additional research addressing the roles of baiting season, bait density, and resource selection will be important to maximize ORV bait uptake by target species in these southeastern landscapes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structures of honeybee-infecting Lake Sinai virus reveal domain functions and capsid assembly with dynamic motions

Understanding the structural diversity of honeybee-infecting viruses is critical to maintain pollinator health and manage the spread of diseases in ecology and agriculture. We determine cryo-EM structures of $T$ = 4 and $T$ = 3 capsids of virus-like particles (VLPs) of Lake Sinai virus (LSV) 2 and delta-N48 LSV1, belonging to tetraviruses, at resolutions of 2.3–2.6 Å in various pH environments. Structural analysis shows that the LSV2 capsid protein (CP) structural features, particularly the protruding domain and C-arm, differ from those of other tetraviruses. The anchor loop on the central β-barrel domain interacts with the neighboring subunit to stabilize homo-trimeric capsomeres during assembly. Delta-N48 LSV1 CP interacts with ssRNA via the rigid helix α1’, α1’–α1 loop, β-barrel domain, and C-arm. Cryo-EM reconstructions, combined with X-ray crystallographic and small-angle scattering analyses, indicate that pH affects capsid conformations by regulating reversible dynamic particle motions and sizes of LSV2 VLPs. C-arms exist in all LSV2 and delta-N48 LSV1 VLPs across varied pH conditions, indicating that autoproteolysis cleavage is not required for LSV maturation. The observed linear domino-scaffold structures of various lengths, made up of trapezoid-shape capsomeres, provide a basis for icosahedral $T$ = 4 and $T$ = 3 architecture assemblies. These findings advance understanding of honeybee-infecting viruses that can cause Colony Collapse Disorder.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SARS-CoV-2 Omicron (B.1.1.529) Infection of Wild White-Tailed Deer in New York City

There is mounting evidence of SARS-CoV-2 spillover from humans into many domestic, companion, and wild animal species. Research indicates that humans have infected white-tailed deer, and that deer-to-deer transmission has occurred, indicating that deer could be a wildlife reservoir and a source of novel SARS-CoV-2 variants. We examined the hypothesis that the Omicron variant is actively and asymptomatically infecting the free-ranging deer of New York City. Between December 2021 and February 2022, 155 deer on Staten Island, New York, were anesthetized and examined for gross abnormalities and illnesses. Paired nasopharyngeal swabs and blood samples were collected and analyzed for the presence of SARS-CoV-2 RNA and antibodies. Of 135 serum samples, 19 (14.1%) indicated SARS-CoV-2 exposure, and 11 reacted most strongly to the wild-type B.1 lineage. Of the 71 swabs, 8 were positive for SARS-CoV-2 RNA (4 Omicron and 4 Delta). Two of the animals had active infections and robust neutralizing antibodies, revealing evidence of reinfection or early seroconversion in deer. Variants of concern continue to circulate among and may reinfect US deer populations, and establish enzootic transmission cycles in the wild: this warrants a coordinated One Health response, to proactively surveil, identify, and curtail variants of concern before they can spill back into humans.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large Scale Rift Valley Fever Outbreaks

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever are directly influenced by large scale flooding associated with the El Ni o/Southern Oscillation. This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential Rift Valley fever outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

Anyamba, Assaf↗
DOE OSTI2023

Advancing the central role of non-model biorepositories in predictive modeling of emerging pathogens

The COVID-19 pandemic demonstrated the insufficiency of a reactive approach to emerging zoonotic pathogens. With spillover increasing in frequency as environments change and the human footprint continues to grow, pandemic prevention will require predictive models that can identify (i) potential zoonoses with a high likelihood of emergence and (ii) environmental or other features that may trigger a shift in host, vector, or pathogen baselines associated with emergence and/or spillover. Artificial intelligence (AI), and particularly its machine learning and deep learning branches, holds enormous potential for detecting shifts in large-scale biodiversity and disease datasets (genomic, ecological, geospatial, etc.). Such algorithms can be trained to identify subtle patterns in large volumes of data to yield insights into complex phenomena for which we have limited knowledge of the true cause(s) or predictor(s), as is the case for emerging infectious diseases.

59 BASIC BIOLOGICAL SCIENCES↗