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Aging affects lung function, predisposing older adults to respiratory diseases; however, the cellular and molecular mechanisms of lung aging are not fully understood. Leveraging single-cell and spatial transcriptomics data from 184 and 70 lung parenchyma samples, respectively, we present an analytical platform to dissect the cell composition, gene expression modules, and regulatory changes linked to multiple hallmarks of lung aging. Our findings show cell type-specific age-association of senescence markers and a decline in alveolar cell proliferation, autocrine WNT signaling, and stemness indicators with advancing age. Analysis of myeloid cells reveals a global reduction in macrophage subsets and a surge in mitochondrial dysfunction and inflammatory signaling. In contrast, lung parenchyma T cells expand with age and exhibit heightened interferon gamma expression, cytotoxic activity, and exhaustion in older lung and blood samples, indicative of age-related immune dysfunction. Cell interaction and spatial analysis demonstrate aberrant myeloid-T cell cross-talk, leading to an increase in T cell chemotaxis and activation. Lastly, we use machine learning to predict lung biological age and identify putative biomarkers of lung aging and disease risk.

Additive manufacturing (AM) has been gaining increased traction in the manufacturing industry due to its ability to fabricate prototypes and end use parts in low volumes at a much lower cost compared to conventional manufacturing processes. There has been research to select an AM process appropriate for fabricating particular parts. However, there is little extant research to select appropriate AM machines even though there is a growing number of AM machines with interesting topologies, structures, and systems. This paper proposes a methodology that aims to assist Technical Experts in selecting a machine for Fused Filament Fabrication (FFF). The methodology is built around a weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which uses the concept of relative closeness and attribute weights to rank the machines. The paper uses Monte Carlo simulations for sensitivity analysis to evaluate the impact of randomizing attribute scoring, perturbing weights assigned, and probability distributions used to model human decision variability. The methodology and the sensitivity analysis were applied to three case studies, with five FFF machines and seven attributes, and top machines ranked for a specific part were found to be largely robust.