TY  - JOUR
A1  - Ellsäßer, Florian
A1  - Röll, Alexander
A1  - Ahongshangbam, Joyson
A1  - Waite, Pierre-André
A1  - Hendrayanto, 
A1  - Schuldt, Bernhard
A1  - Hölscher, Dirk
T1  - Predicting tree sap flux and stomatal conductance from drone-recorded surface temperatures in a mixed agroforestry system — a machine learning approach
JF  - Remote Sensing
N2  - 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.
KW  - transpiration
KW  - method comparison
KW  - UAV
KW  - oil palm
KW  - multiple linear regression
KW  - support vector machine
KW  - random forest
KW  - artificial neural network
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220059
SN  - 2072-4292
VL  - 12
IS  - 24
ER  - 
TY  - JOUR
A1  - Pemp, Daniela
A1  - Geppert, Leo N.
A1  - Wigmann, Claudia
A1  - Kleider, Carolin
A1  - Hauptstein, René
A1  - Schmalbach, Katja
A1  - Ickstadt, Katja
A1  - Esch, Harald L.
A1  - Lehrmann, Leane
T1  - Influence of breast cancer risk factors and intramammary biotransformation on estrogen homeostasis in the human breast
JF  - Archives of Toxicology
N2  - 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.
KW  - estrogens
KW  - human breast
KW  - multiple linear regression
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-235335
SN  - 0340-5761
VL  - 94
ER  -