@article{WunderPempCeciletal.2022,
  author    = {Wunder, Juliane and Pemp, Daniela and Cecil, Alexander and Mahdiani, Maryam and Hauptstein, Ren{\´e} and Schmalbach, Katja and Geppert, Leo N. and Ickstadt, Katja and Esch, Harald L. and Dankekar, Thomas and Lehmann, Leane},
  title     = {Influence of breast cancer risk factors on proliferation and DNA damage in human breast glandular tissues: role of intracellular estrogen levels, oxidative stress and estrogen biotransformation},
  series = {Archives of Toxicology},
  volume    = {96},
  journal   = {Archives of Toxicology},
  number    = {2},
  issn      = {1432-0738},
  doi       = {10.1007/s00204-021-03198-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265343},
  pages     = {673-687},
  year      = {2022},
  abstract  = {Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC- and UHPLC-MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2).},
  language  = {en}
}
@article{PempGeppertWigmannetal.2020,
  author    = {Pemp, Daniela and Geppert, Leo N. and Wigmann, Claudia and Kleider, Carolin and Hauptstein, Ren{\´e} and Schmalbach, Katja and Ickstadt, Katja and Esch, Harald L. and Lehrmann, Leane},
  title     = {Influence of breast cancer risk factors and intramammary biotransformation on estrogen homeostasis in the human breast},
  series = {Archives of Toxicology},
  volume    = {94},
  journal   = {Archives of Toxicology},
  issn      = {0340-5761},
  doi       = {10.1007/s00204-020-02807-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235335},
  pages     = {3013-3025},
  year      = {2020},
  abstract  = {Understanding intramammary estrogen homeostasis constitutes the basis of understanding the role of lifestyle factors in breast cancer etiology. Thus, the aim of the present study was to identify variables influencing levels of the estrogens present in normal breast glandular and adipose tissues (GLT and ADT, i.e., 17β-estradiol, estrone, estrone-3-sulfate, and 2-methoxy-estrone) by multiple linear regression models. Explanatory variables (exVARs) considered were (a) levels of metabolic precursors as well as levels of transcripts encoding proteins involved in estrogen (biotrans)formation, (b) data on breast cancer risk factors (i.e., body mass index, BMI, intake of estrogen-active drugs, and smoking) collected by questionnaire, and (c) tissue characteristics (i.e., mass percentage of oil, oil\%, and lobule type of the GLT). Levels of estrogens in GLT and ADT were influenced by both extramammary production (menopausal status, intake of estrogen-active drugs, and BMI) thus showing that variables known to affect levels of circulating estrogens influence estrogen levels in breast tissues as well for the first time. Moreover, intratissue (biotrans)formation (by aromatase, hydroxysteroid-17beta-dehydrogenase 2, and beta-glucuronidase) influenced intratissue estrogen levels, as well. Distinct differences were observed between the exVARs exhibiting significant influence on (a) levels of specific estrogens and (b) the same dependent variables in GLT and ADT. Since oil\% and lobule type of GLT influenced levels of some estrogens, these variables may be included in tissue characterization to prevent sample bias. In conclusion, evidence for the intracrine activity of the human breast supports biotransformation-based strategies for breast cancer prevention. The susceptibility of estrogen homeostasis to systemic and tissue-specific modulation renders both beneficial and adverse effects of further variables associated with lifestyle and the environment possible.},
  language  = {en}
}
@article{EllsaesserRoellAhongshangbametal.2020,
  author    = {Ells{\"a}ßer, Florian and R{\"o}ll, Alexander and Ahongshangbam, Joyson and Waite, Pierre-Andr{\´e} and Hendrayanto, and Schuldt, Bernhard and H{\"o}lscher, Dirk},
  title     = {Predicting tree sap flux and stomatal conductance from drone-recorded surface temperatures in a mixed agroforestry system — a machine learning approach},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {24},
  issn      = {2072-4292},
  doi       = {10.3390/rs12244070},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220059},
  year      = {2020},
  abstract  = {Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r\(^2\) = 0.87 for trees and r\(^2\) = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration.},
  language  = {en}
}