@book{DiktaScheer2021,
  author    = {Dikta, Gerhard and Scheer, Marsel},
  title     = {Bootstrap Methods: With Applications in R},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-73480-0},
  doi       = {10.1007/978-3-030-73480-0},
  pages     = {XVI, 256 Seiten},
  year      = {2021},
  abstract  = {This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.},
  language  = {en}
}
@article{HeelDiktaBraekers2021,
  author    = {Heel, Mareike van and Dikta, Gerhard and Braekers, Roel},
  title     = {Bootstrap based goodness‑of‑fit tests for binary multivariate regression models},
  series = {Journal of the Korean Statistical Society},
  volume    = {51},
  journal   = {Journal of the Korean Statistical Society},
  publisher = {Springer Nature},
  address   = {Singapur},
  issn      = {2005-2863 (Online)},
  doi       = {10.1007/s42952-021-00142-4},
  pages     = {28 Seiten},
  year      = {2021},
  abstract  = {We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613-641, 1997). In contrast to Stute and Zhu's approach (2002) Stute \& Zhu (Scandinavian J Statist 29:535-545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute \& Zhu (Scandinavian J Statist 29:535-545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.},
  language  = {en}
}
@article{OliveiraMolinnusBegingetal.2021,
  author    = {Oliveira, Danilo A. and Molinnus, Denise and Beging, Stefan and Siqueira Jr, Jos{\´e} R. and Sch{\"o}ning, Michael Josef},
  title     = {Biosensor Based on Self-Assembled Films of Graphene Oxide and Polyaniline Using a Field-Effect Device Platform},
  series = {physica status solidi (a) applications and materials science},
  volume    = {218},
  journal   = {physica status solidi (a) applications and materials science},
  number    = {13},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {1862-6319},
  doi       = {10.1002/pssa.202000747},
  pages     = {1 -- 9},
  year      = {2021},
  abstract  = {A new functionalization method to modify capacitive electrolyte-insulator-semiconductor (EIS) structures with nanofilms is presented. Layers of polyallylamine hydrochloride (PAH) and graphene oxide (GO) with the compound polyaniline:poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PANI:PAAMPSA) are deposited onto a p-Si/SiO2 chip using the layer-by-layer technique (LbL). Two different enzymes (urease and penicillinase) are separately immobilized on top of a five-bilayer stack of the PAH:GO/PANI:PAAMPSA-modified EIS chip, forming a biosensor for detection of urea and penicillin, respectively. Electrochemical characterization is performed by constant capacitance (ConCap) measurements, and the film morphology is characterized by atomic force microscopy (AFM) and scanning electron microscopy (SEM). An increase in the average sensitivity of the modified biosensors (EIS-nanofilm-enzyme) of around 15\% is found in relation to sensors, only carrying the enzyme but without the nanofilm (EIS-enzyme). In this sense, the nanofilm acts as a stable bioreceptor onto the EIS chip improving the output signal in terms of sensitivity and stability.},
  language  = {en}
}
@article{GriegerSchwabedalWendeletal.2021,
  author    = {Grieger, Niklas and Schwabedal, Justus T. C. and Wendel, Stefanie and Ritze, Yvonne and Bialonski, Stephan},
  title     = {Automated scoring of pre-REM sleep in mice with deep learning},
  series = {Scientific Reports},
  volume    = {11},
  journal   = {Scientific Reports},
  number    = {Art. 12245},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-021-91286-0},
  year      = {2021},
  abstract  = {Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.},
  language  = {en}
}
@article{HunkerGossmannRamanetal.2021,
  author    = {Hunker, Jan L. and Gossmann, Matthias and Raman, Aravind Hariharan and Linder, Peter},
  title     = {Artificial neural networks in cardiac safety assessment: Classification of chemotherapeutic compound effects on hiPSC-derived cardiomyocyte contractility},
  series = {Journal of Pharmacological and Toxicological Methods},
  volume    = {111},
  journal   = {Journal of Pharmacological and Toxicological Methods},
  number    = {Article number 107044},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {1056-8719},
  doi       = {10.1016/j.vascn.2021.107044},
  year      = {2021},
  language  = {en}
}