Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “disease”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. Mapping the genetics of gene expression in human microglia has identified several loci associated with disease-associated genetic variants in microglia-specific regulatory elements. However, identifying genetic effects on splicing is challenging because of the use of short sequencing reads. Here, we present the isoform-centric microglia genomic atlas (isoMiGA), which leverages long-read RNA sequencing to identify 35,879 novel microglia isoforms. We show that these isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ancestry meta-analysis of 555 human microglia short-read RNA sequencing samples from 391 donors, and found associations with genetic risk loci in Alzheimer’s and Parkinson’s disease. We nominate several loci that may act through complex changes in isoform and splice-site usage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer’s disease (AD) pathophysiology remains incomplete. Here, for this work, we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases’ environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Albumin in patients with liver disease shows an altered conformation

Abstract Human serum albumin (HSA) constitutes the primary transporter of fatty acids, bilirubin, and other plasma compounds. The binding, transport, and release of its cargos strongly depend on albumin conformation, which is affected by bound ligands induced by physiological and pathological conditions. HSA is both highly oxidized and heavily loaded with fatty acids and bilirubin in chronic liver disease. By employing small-angle X-ray scattering we show that HSA from the plasma of chronic liver disease patients undergoes a distinct opening compared to healthy donors. The extent of HSA opening correlates with clinically relevant variables, such as the model of end-stage liver disease score, bilirubin, and fatty acid levels. Although the mild oxidation of HSA in vitro does not alter overall structure, the alteration of patients’ HSA correlates with its redox state. This study connects clinical data with structural visualization of albumin dynamicity in solution and underlines the functional importance of albumin’s inherent flexibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metabolic Response in Patients With Post-treatment Lyme Disease Symptoms/Syndrome

Abstract Background Post-treatment Lyme disease symptoms/syndrome (PTLDS) occurs in approximately 10% of patients with Lyme disease following antibiotic treatment. Biomarkers or specific clinical symptoms to identify patients with PTLDS do not currently exist and the PTLDS classification is based on the report of persistent, subjective symptoms for ≥6 months following antibiotic treatment for Lyme disease. Methods Untargeted liquid chromatography–mass spectrometry metabolomics was used to determine longitudinal metabolic responses and biosignatures in PTLDS and clinically cured non-PTLDS Lyme patients. Evaluation of biosignatures included (1) defining altered classes of metabolites, (2) elastic net regularization to define metabolites that most strongly defined PTLDS and non-PTLDS patients at different time points, (3) changes in the longitudinal abundance of metabolites, and (4) linear discriminant analysis to evaluate robustness in a second patient cohort. Results This study determined that observable metabolic differences exist between PTLDS and non-PTLDS patients at multiple time points. The metabolites with differential abundance included those from glycerophospholipid, bile acid, and acylcarnitine metabolism. Distinct longitudinal patterns of metabolite abundance indicated a greater metabolic variability in PTLDS versus non-PTLDS patients. Small numbers of metabolites (6 to 40) could be used to define PTLDS versus non-PTLDS patients at defined time points, and the findings were validated in a second cohort of PTLDS and non-PTLDS patients. Conclusions These data provide evidence that an objective metabolite-based measurement can distinguish patients with PTLDS and help understand the underlying biochemistry of PTLDS.

Immunology↗
DOE OSTI2022

A MUC 5 B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality

A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post–COVID-19 pneumonia were performed in MVP subjects. The MUC5B variant rs35705950-T may confer protection in COVID-19 hospitalizations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Normalization of CSF pTau measurement by Aβ 40 improves its performance as a biomarker of Alzheimer’s disease

Alzheimer’s disease (AD)-related tauopathy can be measured with CSF phosphorylated tau (pTau) and tau PET. We aim to investigate the associations between these measurements and their relative ability to predict subsequent disease progression. In 219 cognitively unimpaired and 122 impaired Alzheimer’s Disease Neuroimaging Initiative participants with concurrent amyloid-β (Aβ) PET ( 18 F-florbetapir or 18 F-florbetaben), 18 F-flortaucipir (FTP) PET, CSF measurements, structural MRI, and cognition, we examined inter-relationships between these biomarkers and their predictions of subsequent FTP and cognition changes. The use of a CSF pTau/Aβ 40 ratio eliminated positive associations we observed between CSF pTau alone and CSF Aβ 42 in the normal Aβ range likely reflecting individual differences in CSF production rather than pathology. Use of the CSF pTau/Aβ 40 ratio also increased expected associations with Aβ PET, FTP PET, hippocampal volume, and cognitive decline compared to pTau alone. In Aβ+ individuals, abnormal CSF pTau/Aβ 40 only individuals (26.7%) were 4 times more prevalent (p < 0.001) than abnormal FTP only individuals (6.8%). Furthermore, among individuals on the AD pathway, CSF pTau/Aβ 40 mediates the association between Aβ PET and FTP PET accumulation, but FTP PET is more closely linked to subsequent cognitive decline than CSF pTau/Aβ 40 . Together, these findings suggest that CSF pTau/Aβ 40 may be a superior measure of tauopathy compared to CSF pTau alone, and CSF pTau/Aβ 40 enables detection of tau accumulation at an earlier stage than FTP among Aβ+ individuals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Global Disease Modeling & Forecasting Center [PowerPoint]

The Global Disease Modeling & Forecasting Center works in union with academia, industry, the US Government and global partners to develop, validate, and operationalize epidemiological models to combat infectious diseases. It's mission is to Apply multi-scale modeling and advanced data science to help the world detect, understand, forecast, prevent, and provide decision support in response to emerging and re-emerging disease outbreaks. The presentation explains LANL's role in more detail.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Introgressions of novel diseases resistance genes from Miscanthus into energycane

Sugarcane (Saccharum) is among the world’s leading bioenergy crops. However, modern sugarcane cultivars are derived from a relatively small set of founder genotypes, which has contributed to cultivar susceptibility to diseases. Miscanthus is a close relative of sugarcane that is genetically diverse and a potential source of genes for improving sugarcane. We found that Miscanthus is a source of resistance to four major diseases of sugarcane and we crossed these disease-resistance genes into a predominantly sugarcane genetic background (BC1 generation). Additionally, we found that Miscanthus could also confer genes for chilling-tolerant photosynthesis to sugarcane, which would be highly advantageous for production of energycane in subtropical environments, like the southern coastal plain of the US. Using advanced modeling techniques, we found that standard marker-assisted selection could be effective for breeding resistance conferred by a small number of genes each with large effect (i.e. vertical resistance), but to breed for many genes each of small effect (i.e. horizontal resistance), genomic selection would be the better strategy. Lastly, we learned that Miscanthus can be induced to flower sooner by giving the plants short days (long nights) but that the ideal day length for a given accession depends on its adaptation to its latitude of origin, with tropical accessions requiring shorter days to flower than accessions from high latitudes. This information will facilitate plant breeders’ ability to make crosses between Miscanthus and sugarcane, and enable greater use of Miscanthus as a genetic resource to improve sugarcane.

09 BIOMASS FUELS↗
DOE OSTI2026

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

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

Mathematics and Computing↗
DOE OSTI2024

Evaluation of partial volume correction and analysis of longitudinal [ 18 F]GTP1 tau PET imaging in Alzheimer's disease using linear mixed-effects models

Purpose: We evaluated the impact of partial volume correction (PVC) methods on the quantification of longitudinal [ 18 F]GTP1 tau positron-emission tomography (PET) in Alzheimer's disease and the suitability of describing the tau pathology burden temporal trajectories using linear mixed-effects models (LMEM). Methods: We applied van Cittert iterative deconvolution (VC), 2-compartment, and 3-compartment, and the geometric transfer matrix plus region-based voxelwise methods to data acquired in an Alzheimer's disease natural history study over 18 months at a single imaging site. We determined the optimal PVC method by comparing the standardized uptake value ratio change (%ΔSUVR) between diagnostic and tau burden–level groups and the longitudinal repeatability derived from the LMEM. The performance of LMEM analysis for calculating %ΔSUVR was evaluated in a natural history study and in a multisite clinical trial of semorinemab in prodromal to mild Alzheimer's disease by comparing results to traditional per-visit estimates. Results: The VC, 2-compartment, and 3-compartment PVC methods had similar performance, whereas region-based voxelwise overcorrected regions with a higher tau burden. The lowest within-subject variability and acceptable group separation scores were observed without PVC. The LMEM-derived %ΔSUVR values were similar to the per-visit estimates with lower variability. Conclusion: The results indicate that the tested PVC methods do not offer a clear advantage or improvement over non-PVC images for the quantification of longitudinal [ 18 F]GTP1 PET data. LMEM offers a robust framework for the longitudinal tau PET quantification with low longitudinal test–retest variability. Clinical trial registration: NCT02640092 and NCT03289143.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

Heat-Related Mortality Projections for Cardiovascular and Respiratory Disease Under the Changing Climate in Beijing, China

Because heat-related health effects tend to become more serious at higher temperatures, there is an urgent need to determine the mortality projection of specific heat-sensitive diseases to provide more detailed information regarding the variation of the sensitivity of such diseases. In this study, the specific mortality of cardiovascular and respiratory disease in Beijing was initially projected under five different global-scale General Circulation Models (GCMs) and two Representative Concentration Pathways scenarios (RCPs) in the 2020s, 2050s, and 2080s compared to the 1980s. Multi-model ensembles indicated cardiovascular mortality could increase by an average percentage of 18.4 percent, 47.8 percent, and 69.0 percent in the 2020s, 2050s, and 2080s under RCP 4.5, respectively, and by 16.6 percent, 73.8 percent and 134 percent in different decades respectively, under RCP 8.5 compared to the baseline range. The same increasing pattern was also observed in respiratory mortality. The heat-related deaths under the RCP 8.5 scenario were found to reach a higher number and to increase more rapidly during the 21st century compared to the RCP4.5 scenario, especially in the 2050s and the 2080s. The projection results show potential trends in cause-specific mortality in the context of climate change, and provide support for public health interventions tailored to specific climate-related future health risks.

health↗
NASA NTRS2021

Climate Predicts Geographic and Temporal Variation in Mosquito-Borne Disease Dynamics on Two Continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88%for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

Climate-change ecology↗
DOE OSTI2025

Ticking off Lyme disease: OspA mRNA vaccine halts infection in mouse model

Lyme disease, a condition caused by Borrelia burgdorferi sensu lato and transmitted to humans via ticks, affects approximately 676,000 individuals annually in the United States and Western Europe.1 Currently, there is no approved Lyme vaccine available for human use. A promising study by Tahir et al., published in Molecular Therapy Nucleic Acids, investigated the efficacy of mRNA and subunit vaccines targeting Lyme disease.2 This research demonstrated complete protection from infection in a tick-fed mouse model using an outer surface protein A (OspA) mRNA vaccine. Although mRNA vaccines have shown success against viral pathogens, their clinical application against bacterial diseases has been limited.3 Thus, this study represents an important step toward developing an effective mRNA vaccine against Lyme disease.

He, Wei↗
DOE OSTI2022

Identifying amyloid-related diseases by mapping mutations in low-complexity protein domains to pathologies

Proteins including FUS, hnRNPA2, and TDP-43 reversibly aggregate into amyloid-like fibrils through interactions of their low-complexity domains (LCDs). Mutations in LCDs can promote irreversible amyloid aggregation and disease. We introduce a computational approach to identify mutations in LCDs of disease-associated proteins predicted to increase propensity for amyloid aggregation. We identify several disease-related mutations in the intermediate filament protein keratin-8 (KRT8). Atomic structures of wild-type and mutant KRT8 segments confirm the transition to a pleated strand capable of amyloid formation. Biochemical analysis reveals KRT8 forms amyloid aggregates, and the identified mutations promote aggregation. Aggregated KRT8 is found in Mallory–Denk bodies, observed in hepatocytes of livers with alcoholic steatohepatitis (ASH). We demonstrate that ethanol promotes KRT8 aggregation, and KRT8 amyloids co-crystallize with alcohol. Lastly, KRT8 aggregation can be seeded by liver extract from people with ASH, consistent with the amyloid nature of KRT8 aggregates and the classification of ASH as an amyloid-related condition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

scRNA seq of an F1 cross of Marek’s disease resistant and susceptible chickens identifies allele specific expression signatures enriched in transcription modulators

Abstract Marek’s disease (MD), a T cell lymphoma disease in chickens, is caused by the Marek’s disease virus (MDV) found ubiquitously in the poultry industry. Genetically resistant Line 6 3 (L6) and susceptible Line 7 2 (L7) chickens have been instrumental to research on avian immune system response to MDV infection. In this study we characterized molecular signatures unique to splenic immune cell types across different genetic backgrounds 6 days after infection. Using three populations, L6, L7, and an F1 cross between L6xL7, we evaluated the immune cell transcriptome of responding cell types using single cell RNA sequencing. Several MDV genes were found expressed mainly in cytotoxic T cells while ICP4 and MEQ MDV genes were expressed across infected cell types. Using the F1 we quantified allele specific expression (ASE) of biallelic SNPs and found biased expression of parental alleles specific to immune cell subtypes. We identified 22 SNPs with ASE in response to MDV infection mapped to gene rich regions surrounding 59 genes of critical importance for chromatin remodeling and transcriptional regulation. Histone deacetylase genes (HDAC1 and HDAC8) had increased expression of L6 alleles, while small nuclear RNA genes (SNORA68 and SNORA72) expressed higher levels of L7 alleles with infection in T cell subsets. SNPs with ASE also mapped genes important for an adequate immune response including GNLY (cytotoxic activity) and PDIA3 (component of MHC class I peptide loading complex), and genes known to promote viral replication (MCM5 and EIF3M). These results show that functional variants associated with susceptibility to MD may have a bigger impact in subsets of immune cell types, and by characterizing the transcriptomes of these subtypes we can unravel molecular signatures specific to MD genomic resistance.

Science & Technology - Other Topics↗