@phdthesis{Masek2005,
  author    = {Masek, Pavel},
  title     = {Odor intensity learning in Drosophila},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-15546},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2005},
  abstract  = {It has been known for a long time that Drosophila can learn to discriminate not only between different odorants but also between different concentrations of the same odor. Olfactory associative learning has been described as a pairing between odorant and electric shock and since then, most of the experiments conducted in this respect have largely neglected the dual properties of odors: quality and intensity. For odorant-coupled short-term memory, a biochemical model has been proposed that mainly relies on the known cAMP signaling pathway. Mushroom bodies (MB) have been shown to be necessary and sufficient for this type of memory, and the MB-model of odor learning and short-term memory was established. Yet, theoretically, based on the MB-model, flies should not be able to learn concentrations if trained to the lower of the two concentrations in the test. In this thesis, I investigate the role of concentration-dependent learning, establishment of a concentration-dependent memory and their correlation to the standard two-odor learning as described by the MB-model. In order to highlight the difference between learning of quality and learning of intensity of the same odor I have tried to characterize the nature of the stimulus that is actually learned by the flies, leading to the conclusion that during the training flies learn all possible cues that are presented at the time. The type of the following test seems to govern the usage of the information available. This revealed a distinction between what flies learned and what is actually measured. Furthermore, I have shown that learning of concentration is associative and that it is symmetrical between high and low concentrations. I have also shown how the subjective quality perception of an odor changes with changing intensity, suggesting that one odor can have more than one scent. There is no proof that flies perceive a range of concentrations of one odorant as one (odor) quality. Flies display a certain level of concentration invariance that is limited and related to the particular concentration. Learning of concentration is relevant only to a limited range of concentrations within the boundaries of concentration invariance. Moreover, under certain conditions, two chemically distinct odorants could smell sufficiently similarly such, that they can be generalized between each other like if they would be of the same quality. Therefore, the abilities of the fly to identify the difference in quality or in intensity of the stimuli need to be distinguished. The way how the stimulus is analyzed and processed speaks in favor of a concept postulating the existence of two separated memories. To follow this concept, I have proposed a new form of memory called odor intensity memory (OIM), characterized it and compared it to other olfactory memories. OIM is independent of some members of the known cAMP signaling pathway and very likely forms the rutabaga-independent component of the standard two-odor memory. The rutabaga-dependent odor memory requires qualitatively different olfactory stimuli. OIM is revealed within the limits of concentration invariance where the memory test gives only sub-optimal performance for the concentration differences but discrimination of odor quality is not possible at all. Based on the available experimental tools, OIM seems to require the mushroom bodies the same as odor-quality memory but its properties are different. Flies can memorize the quality of several odorants at a given time but a newly formed memory of one odor interferes with the OIM stored before. In addition, the OIM lasts only 1 to 3 hours - much shorter than the odor-quality memory.},
  subject      = {Taufliege},
  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}
}
@phdthesis{Schwenkert2005,
  author    = {Schwenkert, Isabell},
  title     = {Phenotypic characterization of hangover at the neuromuscular junction},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-14977},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2005},
  abstract  = {Ethanoltoleranz beruht vermutlich auf Ver{\"a}nderung in synaptischer Plastizit{\"a}t; da die Mechanismen, die zu dieser Anpassung der Synapsen f{\"u}hren, in hang-Mutanten offensichtlich defekt sind, war es Ziel dieser Arbeit zu erkl{\"a}ren, wie HANG zu synaptischer Plastizit{\"a}t beitr{\"a}gt. In diesem Zusammenhang war es besonders wichtig herauszufinden, in welchem neuronalen Prozeß HANG eine Rolle spielt. Antik{\"o}rperfarbungen gegen HANG zeigten, da das Protein in allen neuronalen Zellkernen larvaler und adulter Gehirne vorhanden ist. Gehirne der hangAE10 Mutante zeigen keine F{\"a}rbung, was best{\"a}tigt, da diese Tiere Nullmutanten f{\"u}r HANG sind. Eine genauere Analyse der Verteilung von HANG im Zellkern ergab, daß HANG in einem punktartigen Muster an bestimmten Stellen im Kern angereichert ist; diese HANG-Aggregate sind an der Innenseite der Kernmembran lokalisiert und colokalisieren nicht mit dem Chromatin. Auf der Basis dieser Ergebnissen wurde postuliert, daß HANG vermutlich an der Stabilisierung, Prozessierung oder dem Export von mRNAs beteiligt ist. Da synaptische Plastizit{\"a}t gut an den einzelnen Neuronen der neuromuskul{\"a}ren Synapse von Drosophila-Larven studiert werden kann, wurde die Morphologie der Motorneurone dritter Larven am Muskelpaar 6/7 des Segments A4 untersucht. Diese Untersuchungen zeigten, da Boutonanzahl und Axonl{\"a}nge in hangAE10-Larven um 40 \% erh{\"o}ht sind. Außerdem zeigen einige Boutons der hang-Mutanten eine abnormale, sanduhrf{\"o}rmige Form, was darauf hinweist, daß sie nach Initiation der Bouton-Teilung m{\"o}glicherweise in einem halb-separierten Zustand geblieben sind. Die Zunahme an Boutons in den Mutanten ist im wesentlichen auf eine Zunahme der Anzahl der Typ Ib-Boutons zur{\"u}ckzuf{\"u}hren. Die Analyse der Verteilung verschiedener synaptischer Marker in hangover-Mutanten ergab keine Hinweise auf Abnormalit{\"a}ten im Zytoskelett oder in der Ausbildung der pr{\"a}-und postsynaptischen Strukturen. Des weiteren ist die Anzahl der aktiven Zonen relativ zur Boutonoberfl{\"a}che nicht ver{\"a}ndert; da hang-Mutanten aber mehr synaptische Boutons pro synaptischem Terminal besitzen, kann man insgesamt von einer Zunahme der Anzahl der aktiven Zonen ausgehen. Die pr{\"a}synaptische Expression von HANG in den Mutanten rettet die erh{\"o}hte Boutonanzahl und die verl{\"a}ngerten Axone, was ebenfalls beweist, daß die beobachteten synaptischen Defekte auf das Fehlen von HANG und nicht auf Sekund{\"a}rmutationen zur{\"u}ckzuf{\"u}hren sind. Eine postsynaptische Expression der hangover cDNA in den Mutanten dagegen rettet den Ph{\"a}notyp nicht. Die Anzahl der synaptischen Boutons wird unter anderem durch cAMP-Levels bestimmt, welche somit synaptische Plastizit{\"a}t regeln. Da hang-Mutanten eine erh{\"o}hte Boutonanzahl aufweisen, f{\"u}hrte dies zu der Spekulation, daß der Ph{\"a}notyp dieser Mutanten m{\"o}glicherweise auf ver{\"a}nderte cAMPlevels zur{\"u}ckzuf{\"u}hren ist. Um dies zu {\"u}berpr{\"u}fen, wurde die Morphologie der neuromuskul{\"a}ren Synapsen von hangAE10-Larven mit denen von dnc1 verglichen, welche Defekte in der cAMP-Kaskade aufweisen. Einige Aspekte des Ph{\"a}notyps (z. B. die Zunahme der Boutonanzahl und das Verhaltnis von aktiven Zonen pro Boutonfl{\"a}che) sind sehr ¨ahnlich; jedoch unterscheiden sich die beiden Mutanten in anderen morphologischen Aspekten. Die Expression eines UAS-dnc-Transgens in hangover-Mutanten modifizierte den hang-Ph{\"a}notyp ebenfalls nicht. Auf der Basis der Ergebnisse dieser Arbeit wurde ein Modell f{\"u}r die Funktion von HANG erstellt, nach dem dieses Protein vermutlich am Isoform-spezifischen Spleißen bestimmter Transkripte beteiligt ist, deren Produkte f{\"u}r die synaptische Plastizit{\"a}t an der neuromuskul{\"a}ren Synapse ben{\"o}tigt werden.},
  subject      = {Taufliege},
  language  = {en}
}