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    Paternal smoking and maternal secondhand smoke exposure and the effects on the offspring: results from the EHF (Environmental Health Fund) birth cohort by Maya Berlin, Elkana Kohn, Rimona Keidar, Ayelet Livne, Ronella Marom, Amit Ovental, Dror Mandel, Ronit Lubetzky, Moshe Betser, Miki Moskovich, Ariela Hazan, Ludmila Groisman, Efrat Rorman, Matitiahu Berkovitch, Ilan Matok, Laura J. Rosen

    Published 2025-07-01
    “…Half of the partners were smokers, based on the self-reported data. Logistic regression models were used to predict maternal urinary cotinine levels, and potential confounders were included in the model. …”
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  11. 54871

    Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI) by L. Kong, L. Kong, X. Tang, X. Tang, Z. Wang, Z. Wang, Z. Wang, J. Zhu, J. Zhu, J. Li, H. Wu, H. Wu, Q. Wu, H. Chen, H. Chen, L. Zhu, W. Wang, B. Liu, Q. Wang, D. Chen, Y. Pan, Y. Pan, J. Li, J. Li, L. Wu, L. Wu, G. R. Carmichael

    Published 2024-09-01
    “…<p><span id="page4352"/>A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using an ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System. This inventory contains the constrained monthly emissions of NO<span class="inline-formula"><sub><i>x</i></sub></span>, SO<span class="inline-formula"><sub>2</sub></span>, CO, primary PM<span class="inline-formula"><sub>2.5</sub></span>, primary PM<span class="inline-formula"><sub>10</sub></span>, and non-methane volatile organic compounds (NMVOCs) in China from 2013 to 2020, with a horizontal resolution of 15 km <span class="inline-formula">×</span> 15 km. …”
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    Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protoco... by Hanna Reich, Simon Schreynemackers, Rebeka Amin, Sascha Ludwig, Jil Zippelius, Johannes Leimhofer, Tobias Dunker, Elisabeth Schriewer, Angela Carell, Yvonne Weber, Ulrich Hegerl

    Published 2025-06-01
    “…We will apply multilevel correlations, vector-autoregressive models, and Machine Learning approaches to identify individual patterns in the data, particularly in the relationships between biosensor data and self-reported depressive symptoms.Enhancing the understanding of individual disease trajectories through data from smartphones and smartwatches could allow for classical, digital, and self-management interventions for depression to be delivered in a manner and at a time specifically tailored to the individual's needs.Clinical trial registration number: DRKS00032618 (https://drks.de/search/en/trial/DRKS00032618)…”
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