@article{KarulinCaspellDittrichetal.2015,
  author    = {Karulin, Alexey Y. and Caspell, Richard and Dittrich, Marcus and Lehmann, Paul V.},
  title     = {Normal distribution of CD8+ T-cell-derived ELISPOT counts within replicates justifies the reliance on parametric statistics for identifying positive responses},
  series = {Cells},
  volume    = {4},
  journal   = {Cells},
  number    = {1},
  doi       = {10.3390/cells4010096},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-149968},
  pages     = {96-111},
  year      = {2015},
  abstract  = {Accurate assessment of positive ELISPOT responses for low frequencies of antigen-specific T-cells is controversial. In particular, it is still unknown whether ELISPOT counts within replicate wells follow a theoretical distribution function, and thus whether high power parametric statistics can be used to discriminate between positive and negative wells. We studied experimental distributions of spot counts for up to 120 replicate wells of IFN-γ production by CD8+ T-cell responding to EBV LMP2A (426 - 434) peptide in human PBMC. The cells were tested in serial dilutions covering a wide range of average spot counts per condition, from just a few to hundreds of spots per well. Statistical analysis of the data using diagnostic Q-Q plots and the Shapiro-Wilk normality test showed that in the entire dynamic range of ELISPOT spot counts within replicate wells followed a normal distribution. This result implies that the Student t-Test and ANOVA are suited to identify positive responses. We also show experimentally that borderline responses can be reliably detected by involving more replicate wells, plating higher numbers of PBMC, addition of IL-7, or a combination of these. Furthermore, we have experimentally verified that the number of replicates needed for detection of weak responses can be calculated using parametric statistics.},
  language  = {en}
}
@phdthesis{Schindelin2005,
  author    = {Schindelin, Johannes},
  title     = {The standard brain of Drosophila melanogaster and its automatic segmentation},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-15518},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2005},
  abstract  = {In this thesis, I introduce the Virtual Brain Protocol, which facilitates applications of the Standard Brain of Drosophila melanogaster. By providing reliable and extensible tools for the handling of neuroanatomical data, this protocol simplifies and organizes the recurring tasks involved in these applications. It is demonstrated that this protocol can also be used to generate average brains, i.e. to combine recordings of several brains with the same features such that the common features are emphasized. One of the most important steps of the Virtual Insect Protocol is the aligning of newly recorded data sets with the Standard Brain. After presenting methods commonly applied in a biological or medical context to align two different recordings, it is evaluated to what extent this alignment can be automated. To that end, existing Image Processing techniques are assessed. I demonstrate that these techniques do not satisfy the requirements needed to guarantee sensible alignments between two brains. Then, I analyze what needs to be taken into account in order to formulate an algorithm which satisfies the needs of the protocol. In the last chapter, I derive such an algorithm using methods from Information Theory, which bases the technique on a solid mathematical foundation. I show how Bayesian Inference can be applied to enhance the results further. It is demonstrated that this approach yields good results on very noisy images, detecting apparent boundaries between structures. The same approach can be extended to take additional knowledge into account, e.g. the relative position of the anatomical structures and their shape. It is shown how this extension can be utilized to segment a newly recorded brain automatically.},
  subject      = {Taufliege},
  language  = {en}
}