Results for “FACTOR ANALYSIS” — Page 50

DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗

DOE OSTI2024

Spectral Analysis of Regular Material Point Method and its Application to Study High Pressure Reverse Osmosis Membrane Compaction and Embossing

Material Point Method (MPM) is gaining widespread interest in applied continuum mechanics. The fact that all the continuum properties are stored on the particles (or material points) and the governing equations are solved on these material points makes MPM extremely suited to problems involving severe material deformations, such as crack propagation, soil movement, and fluid flows. Despite its popularity, only a few studies have focused on the numerical properties of MPM. This presentation introduces a global spectral analysis of the regular material point method. Contrary to previous studies, the analysis focuses on the numerical properties of the method in the spectral space. The amplification factor is derived as a function of the non- dimensional wave numbers. It provides insights into the stability and dissipative properties of the method for various CFL and Fourier numbers. The effect of the grid shape functions, number of particles per cell and their locations inside the grid cell are also analyzed. The EXAGOOP MPM solver (https://github.com/NREL/Exagoop.git) is developed at the National Renewable Energy Laboratory as a part of the NAWI UHPRO project and is based on the AMReX framework. A single-level, uniform cartesian grid is used as the background mesh, while the particle class in AMReX is used to manage the material point operations. Linear hat and B-splines are used as grid shape functions, while the time integration is performed using explicit Euler time integration. EXAGOOP is both CPU and GPU compatible and has been demonstrated to work well on multiple compute architectures. The performance of EXAGOOP on various computing architectures is presented along with its application to study compaction and embossing of high-pressure reverse osmosis membranes. The MPM solution accurately reproduces the membrane deformation. The deformed pore size and structure simulated using MPM also agree well with experimental SEM images.

material point method↗

DOE OSTI2021

Siloxane Deposition on the Ni-YSZ Solid Oxide Fuel Cell Anode Exposed to Bio-Syngas

Siloxane, a common contaminant present in biogas, is known for adverse effects on cogeneration prime movers. In this study, the siloxane deposition products and mechanism in the solid oxide fuel cell nickel-yttria stabilized zirconia (Ni-YSZ) anode are investigated analytically and experimentally. An SOFC with Ni-YSZ anode and pure Ni/YSZ pellets were exposed to a simulated biogas-reformate fuel with octamethylcyclotetrasiloxane (D4) contamination at 750 °C. The electrochemical characterization results show that the SOFCs performance degradation caused by D4 contamination is irreversible. Morphology and XRD results illustrate that silicon and carbon deposition can both be detected in the anode and pellets. Graphite, SiC and SiO2 are all possible products based on the results of XRD test. Here, according to the formation of graphite and SiC, the new mechanism suggests that carbon is also an essential factor in siloxane contamination of Ni-YSZ anodes besides silicon, which can be explained by the catalytic and electrochemical analysis.

25 ENERGY STORAGE↗

DOE OSTI2026

From Bricks to Clicks: Mapping the White Space in Building Innovation

It is a critical national imperative to transform the buildings sector, yet innovation is impeded by deployment failures that leave promising technologies stranded. Conventional market reports and techno-economic analysis provide an insufficient understanding of markets and resource allocation for emerging building technologies. They omit crucial commercialization factors such as ecosystem maturity and adoption friction, where the coordinated participation of a network of suppliers, contractors, financiers, regulators, and integrators is required to scale solutions. This study addresses these gaps by introducing an evaluation framework grounded in front-line data from six years of the DOE's IMPEL incubator, comprising experience from 300 building-sector innovators and the adjacent, complex ecosystem. Our methodology synthesizes top-down market analysis with bottom-up, practitioner-level data across five megatrends: (M1) Affordable materials and industrialized construction; (M2) Healthy and efficient mechanical systems; (M3) Intelligent building operations; (M4) Buildings as grid assets; and (M5) High-density power and cooling for data centers and therein identify twelve "white space" technology opportunities. Next, we develop a multi-criteria scoring rubric to rank these opportunities based on parameters, i.e., Affordability, Quality of Life, Reliability, and Security, yielding composite ‘Demand’ and ‘Maturity’ indices. Our results indicate that the most significant white spaces may not be incremental products but a new class of ‘Ecosystem Enablers’, such as logistics platforms, orchestration layers, and automated compliance software that solve structural deployment gaps. This paper summarizes this transparent, evidence-based, practitioner-informed evaluation framework for policymakers and investors to re-evaluate policy and resource allocation and unlock scalable market transformation.

Singh, Reshma↗

DOE OSTI2021

De novo transcriptome analysis of white teak (Gmelina arborea Roxb) wood reveals critical genes involved in xylem development and secondary metabolism

Gmelina arborea Roxb is a fast-growing tree species of commercial importance for tropical countries due to multiple industrial uses of its wood. Wood is primarily composed of thick secondary cell walls of xylem cells which imparts the strength to the wood. Identification of the genes involved in the secondary cell wall biosynthesis as well as their cognate regulators is crucial to understand how the production of wood occurs and serves as a starting point for developing breeding strategies to produce varieties with improved wood quality, better paper pulping or new potential uses such as biofuel production. In order to gain knowledge on the molecular mechanisms and gene regulation related with wood development in white teak, a de novo sequencing and transcriptome assembly approach was used employing secondary cell wall synthesizing cells from young white teak trees. For generation of transcriptome, RNA-seq reads were assembled into 110,992 transcripts and 49,364 genes were functionally annotated using plant databases; 5071 GO terms and 25,460 SSR markers were identified within xylem transcripts and 10,256 unigenes were assigned to KEGG database in 130 pathways. Among transcription factor families, C2H2, C3H, bLHLH and MYB were the most represented in xylem. Differential gene expression analysis using leaves as a reference was carried out and a total of 20,954 differentially expressed genes were identified including monolignol biosynthetic pathway genes. The differential expression of selected genes (4CL, COMT, CCoAOMT, CCR and NST1) was validated using qPCR. We report the very first de novo transcriptome of xylem-related genes in this tropical timber species of commercial importance and constitutes a valuable extension of the publicly available transcriptomic resource aimed at fostering both basic and breeding studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Semiclassical theory and the Koopman-van Hove equation *

Abstract The phase space Koopman-van Hove (KvH) equation can be derived from the asymptotic semiclassical analysis of partial differential equations. Semiclassical theory yields the Hamilton–Jacobi equation for the complex phase factor and the transport equation for the amplitude. These two equations can be combined to form a nonlinear semiclassical version of the KvH equation in configuration space. There is a natural injection of configuration space solutions into phase space and a natural projection of phase space solutions onto configuration space. Hence, every solution of the configuration space KvH equation satisfies both the semiclassical phase space KvH equation and the Hamilton–Jacobi constraint. For configuration space solutions, this constraint resolves the paradox that there are two different conserved densities in phase space. For integrable systems, the KvH spectrum is the Cartesian product of a classical and a semiclassical spectrum. If the classical spectrum is eliminated, then, with the correct choice of Jeffreys–Wentzel–Kramers–Brillouin (JWKB) matching conditions, the semiclassical spectrum satisfies the Einstein–Brillouin–Keller quantization conditions which include the correction due to the Maslov index. However, semiclassical analysis uses different choices for boundary conditions, continuity requirements, and the domain of definition. For example, use of the complex JWKB method allows for the treatment of tunneling through the complexification of phase space. Finally, although KvH wavefunctions include the possibility of interference effects, interference is not observable when all observables are approximated as local operators on phase space. Observing interference effects requires consideration of nonlocal operations, e.g. through higher orders in the asymptotic theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Transcriptome Analysis of Drought-Resistant and Drought-Sensitive Sorghum ( Sorghum bicolor ) Genotypes in Response to PEG-Induced Drought Stress

Drought is a major limiting factor of crop yields. In response to drought, plants reprogram their gene expression, which ultimately regulates a multitude of biochemical and physiological processes. The timing of this reprogramming and the nature of the drought-regulated genes in different genotypes are thought to confer differential tolerance to drought stress. Sorghum is a highly drought-tolerant crop and has been increasingly used as a model cereal to identify genes that confer tolerance. Also, there is considerable natural variation in resistance to drought in different sorghum genotypes. Here, we evaluated drought resistance in four genotypes to polyethylene glycol (PEG)-induced drought stress at the seedling stage and performed transcriptome analysis in seedlings of sorghum genotypes that are either drought-resistant or drought-sensitive to identify drought-regulated changes in gene expression that are unique to drought-resistant genotypes of sorghum. Our analysis revealed that about 180 genes are differentially regulated in response to drought stress only in drought-resistant genotypes and most of these (over 70%) are up-regulated in response to drought. Among these, about 70 genes are novel with no known function and the remaining are transcription factors, signaling and stress-related proteins implicated in drought tolerance in other crops. This study revealed a set of drought-regulated genes, including many genes encoding uncharacterized proteins that are associated with drought tolerance at the seedling stage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Optimal 1D Ly α forest power spectrum estimation – II. KODIAQ, SQUAD, and XQ-100

ABSTRACT We measure the 1D Ly α power spectrum P1D from Keck Observatory Database of Ionized Absorption toward Quasars (KODIAQ), The Spectral Quasar Absorption Database (SQUAD), and XQ-100 quasars using the optimal quadratic estimator. We combine KODIAQ and SQUAD at the spectrum level, but perform a separate XQ-100 estimation to control its large resolution corrections in check. Our final analysis measures P1D at scales k < 0.1 s km−1 between redshifts $z$ = 2.0–4.6 using 538 quasars. This sample provides the largest number of high-resolution, high-S/N observations; and combined with the power of optimal estimator it provides exceptional precision at small scales. These small-scale modes (k ≳ 0.02 s km−1), unavailable in Sloan Digital Sky Survey and Dark Energy Spectroscopic Instrument analyses, are sensitive to the thermal state and reionization history of the intergalactic medium, as well as the nature of dark matter. As an example, a simple Fisher forecast analysis estimates that our results can improve small-scale cut-off sensitivity by more than a factor of 2.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Hydropower capacity factors trending down in the United States

Abstract The United States hydropower fleet has faced increasing environmental and regulatory pressures over the last half century, potentially constraining total generation. Here we show that annual capacity factor has declined at four fifths of United States hydropower plants since 1980, with two thirds of decreasing trends significant at p < 0.05. Results are based on an analysis of annual energy generation totals and nameplate capacities for 610 plants (>5 megawatt), representing 87% of total conventional hydropower capacity in the United States. On aggregate, changes in capacity factor imply a fleetwide, cumulative generation decrease of 23% since 1980 before factoring in capacity upgrades—akin to retiring a Hoover Dam once every two to three years. Changes in water availability explain energy decline in only 21% of plants, highlighting the importance of non-climatic drivers of generation, including deterioration of plant equipment as well as changes to dam operations in support of nonpower objectives.

13 HYDRO ENERGY↗

DOE OSTI2020

Ultrathin Plasmonic Tungsten Oxide Quantum Wells with Controllable Free Carrier Densities

Localized surface plasmon resonances (LSPR) of nanostructures can be tuned by controlling their morphology, local dielectric environment, and free carrier concentration. We report the colloidal synthesis of an ~3 tungsten–oxygen (W-O) layer thick (~1 nm), two-dimensional (2D) WO 3-x nanoplatelets (NPLs) (x ≈ 0.55–1.03), which display tunable near-infrared LSPR properties and additionally high free electron density (N e ) that arises predominantly from the large shape factor of 2D NPLs. Importantly, the W to O composition ratios inferred from their LSPR measurements show much higher percentage of oxygen vacancies than those determined by X-ray diffraction analysis, suggesting that the aspect ratio of ultrathin WO 3-x NPLs is the key to producing an unprecedentedly large Ne, although synthesis temperature is also an independent factor. We find that NPL formation is kinetically controlled, whereas thermodynamic parameter manipulation leads to N e values as high as 4.13 × 10 22 cm –3 , which is close to that of plasmonic noble metals, and thus our oxide-based nanostructures can be considered as quasi-metallic. The unique structural properties of 2D nanomaterials along with the high N e of WO 3-x NPLs provide an attractive alternative to plasmonic noble metal nanostructures for various plasmon-driven energy conversions and design of photochromic nanodevices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

New constraints on sodium production in globular clusters from the Na 23 ( He 3 , d ) Mg 24 reaction

The star-to-star anticorrelation of sodium and oxygen is a defining feature of globular clusters, but, to date, the astrophysical site responsible for this unique chemical signature remains unknown. Sodium enrichment within these clusters depends sensitively on reaction rate of the sodium destroying reactions 23 Na(p, γ) and 23 Na(p,α). In this paper, we report the results of a 23 Na( 3 He,d) 24 Mg transfer reaction carried out at Triangle Universities Nuclear Laboratory using a 21 MeV 3 He beam. Astrophysically relevant states in 24 Mg between 11 < E x < 12 MeV were studied using high-resolution magnetic spectroscopy, thereby allowing the extraction of excitation energies and spectroscopic factors. Bayesian methods are combined with the distorted wave Born approximation to assign statistically meaningful uncertainties to the extracted spectroscopic factors. For the first time, these uncertainties are propagated through to the estimation of proton partial widths. Our experimental data are used to calculate the reaction rate. The impact of the new rates are investigated using asymptotic giant branch star models. Furthermore, it is found that while the astrophysical conditions still dominate the total uncertainty, intramodel variations on sodium production from the 23 Na(p, γ) and 23 Na(p,α) reaction channels are a lingering source of uncertainty.

20 ≤ A ≤ 38↗

DOE OSTI2024

Safety First: Stability and Dissipation of Line-tied Force-free Flux Tubes in Magnetized Coronae

Abstract Magnetized plasma columns and extended magnetic structures with both footpoints anchored to a surface layer are an important building block of astrophysical dissipation models. Current loops shining in X-rays during the growth of plasma instabilities are observed in the corona of the Sun and are expected to exist in highly magnetized neutron star magnetospheres and accretion disk coronae. For varying twist and system sizes, we investigate the stability of line-tied force-free flux tubes and the dissipation of twist energy during instabilities using linear analysis and time-dependent force-free electrodynamics simulations. Kink modes ( m = 1) and efficient magnetic energy dissipation develop for plasma safety factors q ≲ 1, where q is the inverse of the number of magnetic field line windings per column length. Higher-order fluting modes ( m > 1) can distort equilibrium flux tubes for q > 1 but induce significantly less dissipation. In our analysis, the characteristic pitch μ ˜ 0 of flux-tube field lines determines the growth rate ( ∝ μ ˜ 0 3 ) and minimum wavelength of the kink instability ( ∝ μ ˜ 0 − 1 ). We use these scalings to determine a minimum flux tube length for the growth of the kink instability for any given μ ˜ 0 . By drawing analogies to idealized magnetar magnetospheres with varying regimes of boundary shearing rates, we discuss the expected impact of the pitch-dependent growth rates for magnetospheric dissipation in magnetar conditions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

EVALUATION OF HRA METHODOLOGIES FOR APPLICATION IN SDP WORK

This study critically evaluates human reliability analysis (HRA) methodologies applicable to regulatory probabilistic safety assessment (PSA) model, with a particular focus on their role in supporting the significance determination process (SDP) in nuclear safety assessment. Firstly, three widely utilized HRA methods – IDHEAS-ECA, SPAR-H, and ASEP/THERP – were qualitatively and quantitatively assessed. Qualitative assessments were conducted using attributes from the NEA/CSNI/R(2015)1 report, while quantitative evaluations employed regression and correlation analyses to compare predicted human error probabilities (HEPs) against empirical data. Results reveal distinct strengths, for example, IDHEAS-ECA’s robust predictive accuracy and K-HRA’s alignment with operational practices. In addition, dependency analysis and recovery analysis were critically evaluated. For dependency analysis, the methods’ handling of inter-task dependencies and their impact on HEPs were examined, while recovery analysis highlighted strategies for mitigating failure events. Furthermore, strategies were proposed to evaluate performance-shaping factors under conditions of reduced human performance, such as stress, fatigue, or cognitive overload, addressing specific challenges faced in SDP evaluations. Human errors from KINS’s operational performance information system event reports were evaluated as a case study. This study identifies gaps and provides actionable insights to ensure their validity and applicability in SDP HRA applications. This paper is a part of research conducted by KINS, and it should be noted that this result does not represent the regulatory position of KINS.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Test of Harmonic Coil Accuracy Using a Multi Vertex Probe

Magnets are integral components of particle accelerators, with different multipoles used to perform various beam conditioning functions, like steering and focusing. Testing accelerator magnets to ensure compliance with specification is a crucial step of fabrication. One of the most popular techniques for verifying magnetic field strength and integrity is the harmonic coil probe. Harmonic coil probes are comprised of passive loops that are rotated around the aperture of a magnet and are used to measure magnetic flux. Oftentimes the measurements of the harmonic coil probe are affected by transverse vibrations along the axis of rotation and torsional vibrations of the position closure sensor, both causing spurious harmonics. To make matters challenging, it is difficult to distinguish between the real harmonics of the magnet and the spurious harmonics during analysis. This research explores the use of a novel configuration of harmonic coil probe, Multi-Vertex Probe (MVP), to address multiple factors that affect the accuracy and repeatability of harmonic coil probe measurement. Most of the results are based on simulation data, in which artificial torsional distortions are introduced and removed with the proposed processing method. In addition to implementing torsional vibration removal in simulation, laboratory analysis regarding the source of vibrations was performed using a pre-fabricated MVP. More specifically, various mechanical conditions were used to explore the standard deviation of main harmonic’s flux amplitude. The conditions included: coupling type (rigid or soft), rotation speed, probe starting position within magnetic field, and cabling location inside the magnetic field. Along with this, the MVP’s measured noise floor in and outside a magnetic field was quantified.

Lofquist, Claire [Northern Illinois U.]↗

DOE OSTI2025

Multi-omics data compendium of pancreatic islet and β cell responses to pro-inflammatory cytokines

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

DOE OSTI2025

Multi-omics data compendium: Data package 10 (Pck010)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

DOE OSTI2025

Multi-omics data compendium: Data package 11 (Pck011)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗

DOE OSTI2025

Multi-omics data compendium: Data package 12 (Pck012)

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Sarkar, Soumyadeep [Pacific Northwest National Lab↗