New York University Press

BackgroundThe integration of multimodal single-cell data enables comprehensive organ reference atlases, yet its impact remains largely unexplored, particularly in complex tissues. Using the kidney as an emblematic example of a complex organ, we perform a systematic evaluation of multimodal single-cell integration strategies, with heart tissue used for additional methodological validation.ResultsWe generate a benchmarking dataset for the renal cortex by integrating 3' and 5' scRNA-seq with joint snRNA-seq and snATAC-seq, profiling 119,744 high-quality nuclei/cells from 19 donors. To align cell identities and enable consistent comparisons, we develop the interpretable machine learning tool scOMM (single-cell Omics Multimodal Mapping) and systematically assess integration strategies. "Horizontal" integration of scRNA and snRNA-seq improves cell-type identification, while "vertical" integration of snRNA-seq and snATAC-seq has an additive effect, enhancing resolution in homogeneous populations and difficult-to-identify states. Global integration is especially effective in identifying adaptive states and rare cell types, including WFDC2-expressing Thick Ascending Limb and Norn cells, previously undetected in kidney atlases.ConclusionsOur work establishes a robust framework for multimodal reference atlas generation, advancing single-cell analysis and extending its applicability to diverse tissues.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13059-026-04002-4.

Abstract We are grateful for Darwish's interest in our paper, Fouad et al. (2025, https://doi.org/10.1029/2024EF004883 ). In this reply, we show that Fouad et al. (2025, https://doi.org/10.1029/2024EF004883 ) did not attribute building collapses in Alexandria solely to hydroclimatic factors, as stated in the comment. Instead, we emphasize that hydroclimatic drivers are presented as accelerators, with other anthropogenic influences explicitly stated in the original paper. Moreover, our response proves that Darwish (2026, https://doi.org/10.1029/2025ef006885 )'s simplistic statistical approach is physically incorrect and obscures absolute risk by normalizing actual building collapse rates to the total number of buildings within a city. Furthermore, our reply shows that the comment conflates the distinct measurement of soil relaxation using shallow isotope mapping at the city scale, as conducted in Fouad et al. (2025, https://doi.org/10.1029/2024EF004883 ), with deep structural geotechnical assessments for foundation design of individual buildings. The utility and complementarity of both methods are already discussed in Fouad et al. (2025, https://doi.org/10.1029/2024EF004883 ). We acknowledge that the statement on the “7,000 at‐risk buildings” is only mentioned in the abstract and is inadvertently missed in the main text; however, the calculation leading to this result is detailed in our supplementary data set and methods. Accordingly, Darwish (2026, https://doi.org/10.1029/2025ef006885 )'s comment, while appreciated, misinterprets Fouad et al. (2025, https://doi.org/10.1029/2024EF004883 ) and overlooks the contemporary literature on Alexandria's hydrogeological and coastal dynamic contexts and their implications for infrastructure instability.

Abstract Urban planners routinely engage with extensive textual materials, such as zoning codes, comprehensive plans, and public comments. The large volume of textual data being generated in cities offers new opportunities to use emerging computational technologies to process and analyze textual data. In this research, we compare how natural language processing (NLP) techniques and large language models (LLMs) compare to human qualitative coding techniques to identify public sentiment and topics contained in public comments about the Minneapolis 2040 upzoning. We use a custom rubric developed in collaboration with urban planners to assess outputs across these different methods, scoring outputs on factors such as accuracy, convergence, creativity, efficiency, and interpretability. Additionally, we conduct interviews with practicing urban planners to understand their perceptions of integrating these computational techniques into their existing workflows. We find that using NLP techniques are helpful in providing urban planners with an aerial view of their data, but require additional human interpretation. In contrast, using LLMs markedly improves efficiency, interpretability, and descriptiveness over traditional NLP techniques, but requires human validation to address concerns related to social biases and equity. We further find that urban planners are open to using new text processing technologies, but have reservations about entirely outsourcing decision-making to AI tools, viewing AI technologies more as “co-pilots” rather than autonomous agents. Our findings underscore the importance of integrating human judgment into using computational tools to develop a more informed, equitable, and reflective practice in an era of expanding urban data and computational technologies.

African American (AA) women are disproportionately affected by hypertension. Discrimination, which can be traumatic, and depressive symptoms are independently associated with blood pressure (BP). We assessed whether the combined influence of discrimination and race-related trauma and depressive symptoms influenced BP over time. We conducted a secondary data analysis of data from a longitudinal cohort study examining factors associated with BP in 250 AA/Black mother-child dyads. Eligible participants were AA/Black mothers, 21 years and older, with a biological child aged 3-5 years. Clinical (BP, body mass index) and psychosocial measures (racial discrimination, major discrimination, race-related stress, depressive symptoms) were examined. Principal component analysis and linear regression were completed to assess the associations of discrimination, race-related trauma, and depressive symptoms with maternal BP. Data from 183 participants were analyzed. Principal component analysis identified 2 key components: overall discrimination and race-related trauma (principal component 1) and depressive symptoms (principal component 2), which explained 83% of data variation. Linear regression analyses revealed principal component 1 was associated with changes in systolic BP (b = 0.92; P = .04), whereas principal component 2 was not (b = 0.31; P = .71). Our findings highlight the need to further explore experiences of discrimination and race-related trauma as a contributing factor for hypertension. In addition, building on existing efforts, future studies should further analyze the presentation of depressive symptoms among AA women to inform the development and validation of culturally sensitive screening tools that effectively capture these symptoms and enhance access to care.

If public humanities is to realize its potential to benefit all in higher education and beyond, it must be published in some way – traditional or unconventional – and be discoverable.