Meeting at the Membrane – Confined Water at Cationic Lipids & Neuronal Growth on Fluid Lipid Bilayers: Meeting at the Membrane – Confined Water at Cationic Lipids &Neuronal Growth on Fluid Lipid Bilayers

Die Zellmembran dient der Zelle nicht nur als äußere Hülle, sondern ist auch an einer Vielzahl von lebenswichtigen Prozessen wie Signaltransduktion oder Zelladhäsion beteiligt. Wasser als integraler Bestandteil von Zellen und der extrazellulären Matrix hat sowohl einen großen Einfluss auf die Struktur von Biomolekülen, als auch selbst besondere Merkmale in eingschränkter Geometrie. Im Rahmen dieser Arbeit wurden zwei Effekte an Modellmembranen untersucht: Erstens der Einfluss des Gegenions an kationischen Lipiden (DODAX, X = F, Cl, Br, I) auf die Eigenschaften des Grenzflächenwassers und zweitens das Vermögen durch Viskositätsänderungen das Wachstum von Nervenzellen anzuregen sowie die einzelnen Stadien der Bildung von neuronalen Netzwerken und deren Optimierung zu charakterisieren. Lipidmultischichten und darin adsorbiertes Grenzflächenwasser wurden mittels Infrarotspektroskopie mit abgeschwächter Totalreflexion untersucht. Nach Charakterisierung von Phasenverhalten und Wasserkapazität der Lipide wurden die Eigenschaften des Wassers durch kontrollierte Hydratisierung bei einem Wassergehalt von einem Wassermolekül pro Lipid verglichen. Durch die geringe Wasserkapazität können in diesem besonderen System direkte Wechselwirkungen zwischen Lipiden und Wasser aus der ersten Hydratationsschale beobachtet werden. Bemerkenswert strukturierte OH-Streckschwingungsbanden in Abhängigkeit des Anions und niedrige IR-Ordnungsparameter zeigen, dass stark geordnete, in ihrer Mobilität eingeschränkte Wassermoleküle an DODAX in verschiedenen Populationen mit unterschiedlich starken Wasserstoffbrückenbindungen existieren und sich vermutlich in kleinen Clustern anordnen. Die zweite Fragestellung hatte zum Ziel, das Wachstum von Nervenzellen auf Membranen zu beleuchten. Auf der Ebene einzelner Zellen wurde untersucht, ob sich in Analogie zu den bisher verwendeten elastischen Substraten, die Viskosität von Membranen als neuartiger physikalischer Stimulus dafür eignet, das mechanosensitive Verhalten von Neuronen zu modulieren. Das Wachstum der Neuronen wurde auf substrat- und polymergestützten Lipiddoppelschichten mittels Phasenkontrastmikroskopie beobachtet. Die Quantifizierung der Neuritenlängen, -auswuchsgeschwindigkeiten und -verzweigungen zeigten kaum signifikante Unterschiede. Diffusionsmessungen (FRAP) ergaben, dass entgegen der Erwartungen, die Substrate sehr ähnliche Fluiditäten aufweisen. Die Betrachtung der zeitlichen Entwicklung des kollektiven Neuronenwachstums, also der Bildung von komplexen Netzwerken, offenbarte robuste „Kleine-Welt“-Eigenschaften und darüber hinaus unterschiedliche Stadien. Diese wurden durch graphentheoretische Analyse beschrieben, um anhand typischer Größen wie dem Clusterkoeffizienten und der kürzesten Pfadlänge zu zeigen, wie sich die Neuronen in einem frühen Stadium vernetzen, im Verlauf eine maximale Komplexität erreichen und letztlich das Netzwerk durch effiziente Umstrukturierung hinsichtlich kurzer Pfadlängen optimiert wird

A Review of Evolution, Behavior, and Vision with an Experimental Evolution Study on Color Vision in Drosophila melanogaster

The first chapter of this thesis is to take a piece by piece look at the factors that contributed to the experimental evolution study that will be discussed in Chapter 2. Behavior, how that can affect experimental studies, and how biases can affect sensory systems and preference in subject species. Specifically visual sensory systems are described in detail, from the possible evolutionary histories, to major components that contribute to eye structure, form, and/or abilities. We discuss how to define color vision, and what are the prerequisites for color vision in species

Indexing and Retrieval of 3D Articulated Geometry Models

In this PhD research study, we focus on building a content-based search engine for 3D articulated geometry models. 3D models are essential components in nowadays graphic applications, and are widely used in the game, animation and movies production industry. With the increasing number of these models, a search engine not only provides an entrance to explore such a huge dataset, it also facilitates sharing and reusing among different users. In general, it reduces production costs and time to develop these 3D models. Though a lot of retrieval systems have been proposed in recent years, search engines for 3D articulated geometry models are still in their infancies. Among all the works that we have surveyed, reliability and efficiency are the two main issues that hinder the popularity of such systems. In this research, we have focused our attention mainly to address these two issues. We have discovered that most existing works design features and matching algorithms in order to reflect the intrinsic properties of these 3D models. For instance, to handle 3D articulated geometry models, it is common to extract skeletons and use graph matching algorithms to compute the similarity. However, since this kind of feature representation is complex, it leads to high complexity of the matching algorithms. As an example, sub-graph isomorphism can be NP-hard for model graph matching. Our solution is based on the understanding that skeletal matching seeks correspondences between the two comparing models. If we can define descriptive features, the correspondence problem can be solved by bag-based matching where fast algorithms are available. In the first part of the research, we propose a feature extraction algorithm to extract such descriptive features. We then convert the skeletal matching problems into bag-based matching. We further define metric similarity measure so as to support fast search. We demonstrate the advantages of this idea in our experiments. The improvement on precision is 12\% better at high recall. The indexing search of 3D model is 24 times faster than the state of the art if only the first relevant result is returned. However, improving the quality of descriptive features pays the price of high dimensionality. Curse of dimensionality is a notorious problem on large multimedia databases. The computation time scales exponentially as the dimension increases, and indexing techniques may not be useful in such situation. In the second part of the research, we focus ourselves on developing an embedding retrieval framework to solve the high dimensionality problem. We first argue that our proposed matching method projects 3D models on manifolds. We then use manifold learning technique to reduce dimensionality and maximize intra-class distances. We further propose a numerical method to sub-sample and fast search databases. To preserve retrieval accuracy using fewer landmark objects, we propose an alignment method which is also beneficial to existing works for fast search. The advantages of the retrieval framework are demonstrated in our experiments that it alleviates the problem of curse of dimensionality. It also improves the efficiency (3.4 times faster) and accuracy (30\% more accurate) of our matching algorithm proposed above. In the third part of the research, we also study a closely related area, 3D motions. 3D motions are captured by sticking sensor on human beings. These captured data are real human motions that are used to animate 3D articulated geometry models. Creating realistic 3D motions is an expensive and tedious task. Although 3D motions are very different from 3D articulated geometry models, we observe that existing works also suffer from the problem of temporal structure matching. This also leads to low efficiency in the matching algorithms. We apply the same idea of bag-based matching into the work of 3D motions. From our experiments, the proposed method has a 13\% improvement on precision at high recall and is 12 times faster than existing works. As a summary, we have developed algorithms for 3D articulated geometry models and 3D motions, covering feature extraction, feature matching, indexing and fast search methods. Through various experiments, our idea of converting restricted matching to bag-based matching improves matching efficiency and reliability. These have been shown in both 3D articulated geometry models and 3D motions. We have also connected 3D matching to the area of manifold learning. The embedding retrieval framework not only improves efficiency and accuracy, but has also opened a new area of research

Embodied gestures

This is a book about musical gestures: multiple ways to design instruments, compose musical performances, analyze sound objects and represent sonic ideas through the central notion of ‘gesture’. The writers share knowledge on major research projects, musical compositions and methodological tools developed among different disciplines, such as sound art, embodied music cognition, human-computer interaction, performative studies and artificial intelligence. They visualize how similar and compatible are the notions of embodied music cognition and the artistic discourses proposed by musicians working with ‘gesture’ as their compositional material. The authors and editors hope to contribute to the ongoing discussion around creative technologies and music, expressive musical interface design, the debate around the use of AI technology in music practice, as well as presenting a new way of thinking about musical instruments, composing and performing with them