Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PATHOLOGY”

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 OSTI2023

Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incompletely understood. Here, we use deep learning and artificial intelligence to discover a mathematical model for the progression of Alzheimer's disease using longitudinal tau positron emission tomography from the Alzheimer's Disease Neuroimaging Initiative database. Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading. First, we demonstrate the potential of our model and parameter discovery on synthetic data. Then, we apply our method to discover the best model and parameters to explain tau imaging data from 46 individuals who are likely to develop Alzheimer's disease and 30 healthy controls. Our symbolic regression discovers different misfolding models f(c) for two groups, with a faster misfolding for the Alzheimer's group, f(c) = 0.23c 3 – 1.34c 2 + 1.11c, than for the healthy control group, f(c) = –c 3 + 0.62c 2 + 0.39c. Our results suggest that PINNs, supplemented by symbolic regression, can discover a reaction-diffusion type model to explain misfolded tau protein concentrations in Alzheimer's disease. Furthermore, we expect our study to be the starting point for a more holistic analysis to provide image-based technologies for early diagnosis, and ideally early treatment of neurodegeneration in Alzheimer's disease and possibly other misfolding-protein based neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Respecting causality for training physics-informed neural networks

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In this work we attribute this shortcoming to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. We argue that this is a fundamental limitation and a key source of error that can ultimately steer PINN models to converge towards erroneous solutions. We address this pathology by proposing a simple re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. We demonstrate that this simple modification alone is enough to introduce significant accuracy improvements, as well as a practical quantitative mechanism for assessing the convergence of a PINNs model. Here we provide state-of-the-art numerical results across a series of benchmarks for which existing PINNs formulations fail, including the chaotic Lorenz system, the Kuramoto–Sivashinsky equation in the chaotic regime, and the Navier–Stokes equations. To the best of our knowledge, this is the first time that PINNs have been successful in simulating such systems, introducing new opportunities for their applicability to problems of industrial complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Motif-centric phosphoproteomics to target kinase-mediated signaling pathways

Identifying cellular phosphorylation pathways based on kinase-substrate relationships is a critical step to understanding the regulation of physiological functions in cells. Mass spectrometry-based phosphoproteomics workflows have made it possible to comprehensively collect information on individual phosphorylation sites in a variety of samples. However, there is still no generic approach to uncover phosphorylation networks based on kinase-substrate relationships in rare cell populations. Here, we describe a motif-centric phosphoproteomics approach combined with multiplexed isobaric labeling, in which in vitro kinase reactions are used to generate targeted phosphopeptides, which are spiked into one of the isobaric channels to increase detectability. Proof-of-concept experiments demonstrate selective and comprehensive quantification of targeted phosphopeptides by using multiple kinases for motif-centric channels. More than 7,000 tyrosine phosphorylation sites were quantified from several tens of micrograms of starting materials. This approach enables the quantification of multiple phosphorylation pathways under physiological or pathological regulation in a motif-centric manner.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A susceptibility gene signature for ERBB2-driven mammary tumour development and metastasis in collaborative cross mice

Background: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. Methods: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice. Findings: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. Interpretation: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

siRNA high throughput screening identifies regulators of chloropicrin and hydrogen fluoride injury in human corneal epithelial cell models

Corneal injuries induced by various toxicants result in similar clinical presentations such as corneal opacity and neovascularization. Many studies suggest that several weeks post-exposure a convergence of the molecular mechanisms drives these progressive pathologies. However, chemical agents vary in toxicological properties, and early molecular responses are anticipated to be somewhat dissimilar for different toxicants. In this report we chose 3120 targets from the Dharmacon Human Druggable genome to screen for chloropicrin (CP) and hydrogen fluoride (HF) corneal injury as we hypothesized that targets identified in vitro may be effective as therapeutic targets in future studies. Human immortalized corneal epithelial cells (SV40-HCEC) were used for screening. Cell viability and IL-8 were analyzed to down-select hits into validation studies, where multiplex cytokine analysis and high content analysis were performed to understand toxicant effect and target function. Some endpoints were also evaluated in a second human immortalized corneal epithelial cell line, TCEpi. Over 20 targets entered validation studies for CP and HF; of these, only three targets were shared: NR3C1, RELA, and KMT5A. These findings suggest that early molecular responses to different toxicants may be somewhat distinctive and present dissimilar targets for possible early intervention.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The Influence of Severe Radiation-Induced Lymphopenia on Overall Survival in Solid Tumors: A Systematic Review and Meta-Analysis

Emerging evidence suggests a detrimental prognostic association between radiation-induced lymphopenia (RIL) and pathologic response, progression-free survival, and overall survival (OS) in patients who undergo radiation therapy for cancer. The aim of this study was to systematically review and meta-analyze the prognostic impact of RIL on OS in patients with solid tumors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

High-Content Clonogenic Survival Screen to Identify Chemoradiation Sensitizers

The combination of cytotoxic chemotherapy with radiation therapy (CRT) has resulted in significant improvements in clinical outcomes for patients with many locally advanced unresectable cancers. Only a small proportion of patients achieve pathologic complete responses to CRT; combination of CRT with targeted agents offers the promise of further improving treatment responses. However, numerous clinical trials have failed to show an improvement in clinical outcomes with the addition of targeted agents. To increase the accessibility of our screening method and accelerate the pace at which novel combinations with CRT are identified and incorporated into standard practices for treatments, we report details on screening method optimization, data generation, and downstream data analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Interplay between liver and blood stages of Plasmodium infection dictates malaria severity via γδ T cells and IL-17-promoted stress erythropoiesis

Plasmodium replicates within the liver prior to reaching the bloodstream and infecting red blood cells. Because clinical manifestations of malaria only arise during the blood stage of infection, a perception exists that liver infection does not impact disease pathology. By developing a murine model where the liver and blood stages of infection are uncoupled, we showed that the integration of signals from both stages dictated mortality outcomes. This dichotomy relied on liver stage-dependent activation of Vγ4 + γδ T cells. Subsequent blood stage parasite loads dictated their cytokine profiles, where low parasite loads preferentially expanded IL-17-producing γδ T cells. IL-17 drove extra-medullary erythropoiesis and concomitant reticulocytosis, which protected mice from lethal experimental cerebral malaria (ECM). Adoptive transfer of erythroid precursors could rescue mice from ECM. Modeling of γδ T cell dynamics suggests that this protective mechanism may be key for the establishment of naturally acquired malaria immunity among frequently exposed individuals.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Effects of ingested nanocellulose on intestinal microbiota and homeostasis in Wistar Han rats

Micron scale crystalline and fibrillar cellulose materials are “generally regarded as safe” (GRAS) as binders and thickeners in food products. Yet, partly due to a lack of relevant toxicological data, nanocellulose (NC) materials, which have unique properties that can be exploited to improve food quality and safety, have yet to receive FDA approval as food ingredients. Results of in vitro and in vivo toxicological studies of ingested NC, detailed in a recent companion report, revealed minimal acute in vitro cytotoxicity, and no toxicity in a subacute rat gavage model, suggesting that NC materials are non-hazardous. However, ingested materials may also modulate gut microbial populations, or alter aspects of intestinal function not evaluated in standard totoxicity testing, which could have important health implications. Here, we report the results of studies of the effects of ingested cellulose nanofibrils (CNF) on the fecal microbiome and metabolome, intestinal (ileal) epithelial cell mRNA expression of cell junction genes, and ileal cytokine production. Feces, plasma, and ileum samples were collected from Wistar Han rats before and after five weeks of biweekly gavages of water or cream, with or without 1% w/w CNF. Analysis of fecal microbial populations revealed that CNF caused changes in both genus and species diversity, with specific effects on species that produce short chain fatty acids, and that have been associated with increased IgA production and elevation of insulin levels. Fecal metabolomic analysis revealed relatively few effects of CNF, with significant changes in the levels of only ten metabolites out of 366 measured. Exposure to CNF also caused differential regulation of mRNA expression of several genes involved in epithelial cell junctions, and increased production of cytokines that both promote and inhibit proliferation of CD8 T cells. These perturbations in intestinal homeostasis contrast somewhat with the absence of in vitro and in vivo toxicity observed previously but would appear to represent minor effects. Additionally, the gut microbiome is highly sensitive to ingested substances, and such disturbances of the intestinal microbial ecosystem do not necessarily represent or predict meaningful pathology. Further studies, including chronic feeding studies, are needed to assess the real health implications, if any, of these changes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Senolysis induced by 25-hydroxycholesterol targets CRYAB in multiple cell types

Cellular senescence is a driver of many age-related pathologies. There is an active search for pharmaceuticals termed senolytics that can mitigate or remove senescent cells in vivo by targeting genes that promote the survival of senescent cells. We utilized single-cell RNA sequencing to identify CRYAB as a robust senescence-induced gene and potential target for senolysis. Using chemical inhibitor screening for CRYAB disruption, we identified 25-hydroxycholesterol (25HC), an endogenous metabolite of cholesterol biosynthesis, as a potent senolytic. We then validated 25HC as a senolytic in mouse and human cells in culture and in vivo in mouse skeletal muscle. Thus, 25HC represents a potential class of senolytics, which may be useful in combating diseases or physiologies in which cellular senescence is a key driver.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

When and why PINNs fail to train: A neural tangent kernel perspective

Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known about why such models sometimes fail to train at all. Here in this work, we aim to investigate these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent. Specifically, we derive the NTK of PINNs and prove that, under appropriate conditions, it converges to a deterministic kernel that stays constant during training in the infinite-width limit. This allows us to analyze the training dynamics of PINNs through the lens of their limiting NTK and find a remarkable discrepancy in the convergence rate of the different loss components contributing to the total training error. To address this fundamental pathology, we propose a novel gradient descent algorithm that utilizes the eigenvalues of the NTK to adaptively calibrate the convergence rate of the total training error. Finally, we perform a series of numerical experiments to verify the correctness of our theory and the practical effectiveness of the proposed algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Flow and transport in three-dimensional discrete fracture matrix models using mimetic finite difference on a conforming multi-dimensional mesh

Here, we present a comprehensive workflow to simulate single-phase flow and transport in fractured porous media using the discrete fracture matrix approach. The workflow has three primary parts: (1) a method for conforming mesh generation of and around a three-dimensional fracture network, (2) the discretization of the governing equations using a second-order mimetic finite difference method, and (3) implementation of numerical methods for high-performance computing environments. A method to create a conforming Delaunay tetrahedralization of the volume surrounding the fracture network, where the triangular cells of the fracture mesh are faces in the volume mesh, that addresses pathological cases which commonly arise and degrade mesh quality is also provided. Our open-source subsurface simulator uses a hierarchy of process kernels (one kernel per physical process) that allows for both strong and weak coupling of the fracture and matrix domains. We provide verification tests based on analytic solutions for flow and transport, as well as numerical convergence. We also provide multiple expositions of the method in complex fracture networks. In the first example, we demonstrate that the method is robust by considering two scenarios where the fracture network acts as a barrier to flow, as the primary pathway, or offers the same resistance as the surrounding matrix. In the second test, flow and transport through a three-dimensional stochastically generated network containing 257 fractures is presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The congenital APOA1 K107del mutation disrupts the lipid-free conformation of monomeric APOA1 and impairs oligomerization

Apolipoprotein A-I (APOA1) oligomerization is thought to be essential for high-density lipoprotein (HDL) formation and metabolism. Naturally occurring mutations can disrupt normal APOA1 folding and self-association, leading to dysfunctional HDL formation and cardiovascular disease. The congenital APOA1 variant p.K131del (APOA1 K107del ) has been associated with cardiovascular pathologies such as low HDL-cholesterol levels and aortic amyloidosis, and multiple studies indicate structural changes in APOA1 conformation underlie associated dysfunction. In the current study, we confirmed that APOA1 K107del exhibits no notable defect in lipid-binding. However, using polyacrylamide gel electrophoresis (PAGE) and size-exclusion chromatography (SEC), we found that loss of lysine 107 resulted in a remarkable shift in the distribution of APOA1 oligomers with a much higher proportion of monomers present in APOA1 K107del compared to wild-type APOA1. Further investigation using quantitative cross-linking revealed a major disruption of interactions in helical regions reported to participate in domain swaps necessary for proper self-association. This structural disruption appears to impair N- and C-termini interactions and dynamics that lead to non-specific aggregation. In conclusion, these findings support the hypothesis that lysine 107 is critical for proper folding and self-association of lipid-free APOA1 which could impact HDL biogenesis.

APOA1 (p.K131del)↗
DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deterministic chaos in the self-assembly of β sheet nanotubes from an amphipathic oligopeptide

The self-assembly of designed peptides into filaments and other higher-order structures has been the focus of intense interest because of the potential for creating new biomaterials and biomedical devices. These peptide assemblies have also been used as models for understanding biological processes, such as the pathological formation of amyloid. Here we investigate the assembly of an octapeptide sequence, Ac-FKFEFKFE-NH2, motivated by prior studies that demonstrated that this amphipathic beta strand peptide self-assembled into fibrils and biocompatible hydrogels. Using high-resolution cryoelectron microscopy (cryo-EM), we are able to determine the atomic structure for two different coexisting forms of the fibrils, containing four and five beta sandwich protofilaments, respectively. Surprisingly, the inner walls in both forms are parallel beta sheets, while the outer walls are antiparallel beta sheets. Our results demonstrate the chaotic nature of peptide self-assembly and illustrate the importance of cryo-EM structural analysis to understand the complex phase behavior of these materials at near-atomic resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Cerebellar dysfunction in a mouse model of childhood-onset manganese-induced dystonia parkinsonism

Humans with pathogenic variants of the manganese (Mn) transporter gene SLC39A14 exhibit highly elevated brain Mn concentrations and childhood-onset dystonia-parkinsonism. Here we show that Slc39a14-knockout (KO) mice, a preclinical model of the disease with elevated Mn concentrations in the CB, express deficits in physiological tremor implicating cerebellar (CB) dysfunction. Imaging of intracellular Mn in Purkinje cells (PCs) using synchrotron-based X-ray fluorescence microscopy confirmed highly elevated Mn concentrations in the PCs of Slc39a14-KO mice. To determine biological pathways altered in the CB of Slc39a14-KO mice relative to wildtype (WT), we performed RNA sequencing and discovered significant upregulation of pathways and genes regulating immune response and cell death. To substantiate these findings, we performed quantitative autoradiography of the neuroinflammation biomarker Translocator Protein 18 kDa (TSPO) which was significantly increased in the CB of Slc39a14-KO mice relative to WT. The latter findings were confirmed via immunostaining with the microglial marker Iba-1, revealing widespread microglia activation and clustering in the CB cortex. Immunostaining for cleaved caspase-3 (cCASP3), a marker of apoptosis, showed increased number of PCs with positive immunolabeling for cCASP3 in Slc39a14-KO mice relative to WT. Degeneration of PCs was confirmed by Hematoxylin and Eosin (H&E) staining. Lastly, functional electrophysiological assessment of CB neurocircuitry revealed a marked decrease in firing rates of cerebellar nuclei (CN) neurons and increased variability of PC simple spikes firing. Collectively, these findings show, for the first time, Mn-induced PC degeneration and dysfunctional CB circuitry in Slc39a14-KO mice providing additional evidence for the pathological underpinnings of the dystonia-like movements, balance, and gait abnormalities in SLC39A14 mutation carriers.

36 MATERIALS SCIENCE↗