We showcase a hybrid modeling framework that embeds machine learning (ML) inference into the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (SPEAR) climate model for online sea ice bias correction during a set of global fully coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully coupled simulations: HybridCPL (couple trained; with feedbacks) and HybridIO (ice ocean trained; without feedbacks). Relative to SPEAR, HybridCPL systematically reduces seasonal forecast errors in the Arctic and considerably reduces Antarctic errors for target months May to December, with >2× error reduction in 4- to 6-month lead forecasts of Antarctic winter sea ice extent. Meanwhile, HybridIO suffers from out-of-sample behavior that can trigger a chain of Southern Ocean feedbacks, leading to ice-free Antarctic summers. Our results emphasize that ML can demonstrably improve numerical sea ice prediction capabilities and that exposing ML models to coupled ice-atmosphere-ocean processes is essential for generalization in fully coupled simulations.
BACKGROUND:We previously identified three clinically relevant immune [i.e., low-tumor-infiltrating lymphocyte (TIL), high-TIL, and high-interferon-stimulated gene (ISG)] subtypes of luminal breast cancer using RNA sequencing in a cohort of women from Hong Kong. In this study, we evaluated whether standard, in situ immunohistochemistry (IHC), a more clinically accessible approach, could capture similar immune heterogeneity across intrinsic (PAM50) subtypes. METHODS:In 148 patients with invasive breast cancer, we performed dual IHC staining for eight markers (CD3, CD8, CD20, FOXP3, CD68, CD163, PDL1, and IDO1) with pan-cytokeratin and quantified immune-positive cells in stromal regions by digital image analysis. RESULTS:IHC marker expression strongly correlated with gene expression-based immune and intrinsic subtypes. Compared with luminal tumors classified as low-TIL, high-TIL tumors showed higher levels of CD3, CD8, CD20, PDL1, and IDO1. Similarly, high-ISG tumors expressed higher CD3, CD8, CD20, PDL1, and IDO1 than low-TIL tumors but were distinguished from high-TIL tumors by elevated CD163. Immune marker expression was generally higher in HER2-enriched and basal-like tumors than in luminal A and B tumors, though substantial variability existed within each subtype. Luminal B tumors expressed higher levels of CD3, CD8, CD20, and CD163 compared with luminal A tumors. Notably, immune infiltration was more strongly aligned with aggressive tumor features such as high grade, risk of recurrence scores, and TP53 mutations than with estrogen receptor/progesterone receptor expression. CONCLUSIONS:These findings confirm that subsets of luminal tumors harbor inflamed or macrophage-driven immune microenvironments. IMPACT:Parsimonious IHC panels can perform immune classification, supporting their potential utility in refining subtype stratification and informing clinical decision-making, particularly in resource-limited settings.
Nitrogen Dioxide (NO2) is a key component of tropospheric chemistry and air quality, yet large uncertainties persist in regional NOx emissions across rapidly developing megacities in Southeast Asia. Observations from the Geostationary Emissions Monitoring Spectrometer (GEMS) provide new constraints on anthropogenic NO2 variability, while the 2024 NASA Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, offers an extensive, independent dataset for model evaluation. Here, we examine air quality in Bangkok using coarse (20 km) and high-resolution (4 km) WRF-Chem simulations during ASIA-AQ. We develop a top-down framework that uses hourly GEMS NO2 columns to derive constraints on the daytime cycle of NOx emissions. Emissions are first estimated from GEMS using a Cross-Sectional Flux (CSF) inversion and then incorporated into WRF-Chem through a novel optimization that reshapes the magnitude and daytime structure of NOx while accounting for lifetime and satellite vertical sensitivity. GEMS-constrained NOx emissions for March 2024 are estimated to range from 2.7 to 4.3 kt month−1 after accounting for known low biases in the GEMS retrievals. Re-running WRF-Chem with the updated emissions leads to substantial improvements in modeled NO2 magnitude and temporal variability when evaluated against independent ground-based, Pandora, and airborne measurements. Remaining negative biases are consistent with a systematic low bias in the GEMS v3 NO2 product, highlighting the importance of multi-platform evaluation using independent observations. Together, these results demonstrate the value of hourly geostationary observations combined with high-resolution modeling as a scalable pathway for improving urban NOx emissions estimates and air quality simulations in Southeast Asia.
The Army Game Studio (AGS) team at the U.S. Army Combat Capabilities Development Command Aviation & Missile Center (DEVCOM AvMC) Software, Simulation, Systems Engineering & Integration Directorate (S3I) on Redstone Arsenal uses the Unreal commercial game engine to develop systems supporting training, simulation, education, and research. To enhance the depiction of sensor views of the environment, we conducted a pilot study into artificial intelligence and machine learning (AI/ML) models to produce realistic infrared (IR) imagery. The end goal was to generate IR scenes in Rocket IG without having to create new IR textures for every vehicle, which will reduce the cost of generating IR scenes and improve scene accuracy. After initial research, IRSim generates IR scenes that correspond to still electro-optical (EO) images. For additional information, please contact AGS by email at hello@armygamestudio.com.
As spacecraft become more software-driven and interconnected, onboard flight software is an increasingly important security boundary. Popular flight software architectures often treat onboard components as trusted peers, simplifying integration while limiting internal isolation and access control. We analyze NASA's Core Flight Software (cFS) to examine how authority, identity, communication, observability, and persistence are distributed across onboard components. Using NASA's flight-representative NOS3 simulator, we validate these weaknesses through five experiments implemented with a malicious onboard component that abuses legitimate architectural privileges. We then compare cFS with other modular flight software frameworks to identify recurring trust assumptions and architectural weaknesses. Our results show that a single compromised component can exploit broadly shared authority in ways that are difficult to distinguish from legitimate behavior. We conclude with architectural implications and discuss mechanisms for strengthening internal trust boundaries in future flight software systems.
