Magnetic nanoparticles (MNPs) have found use in a wide variety of biomedical applications including hyperthermia, imaging and drug delivery. Certain physical properties, such as the ability to generate heat in response to an alternating magnetic field, make these structures ideal for such purposes. This study's objective was to elucidate the mechanisms primarily responsible for RF MNP heating and determine how such processes affect polymer solutions that might be useful in drug delivery. 15-20 nm magnetite (Fe3O4) nanoparticles at 0.2% and 0.5% concentrations were heated with RF fields of different strengths (200 Oe, 400 Oe and 600 Oe) in water and in 0.5% gellan gum solution. Mixing and fan cooling were used in an attempt to improve accuracy of data collection. Specific absorption rate (SAR) values were determined experimentally for each combination of solvent, concentration and field strength. Theoretical calculation of SAR was performed using a model based on linear response theory. Mixing yielded greater precision in experimental determination of SAR while the effects of cooling on this parameter were negligible. Solutions with gellan gum displayed smoother heating over time but no significant changes in SAR values. This was attributed to low polymer concentration and lack of structural phase transition. The LRT model was found to be adequate for calculating SAR at low polymer concentration and was useful in identifying Neel relaxation as the dominant heating process. Heating trials with MNPs in 2% agar confirmed Neel relaxation to be primarily responsible for heat generation in the particles studied.

Sensory neurons assemble to form networks that process inputs coming from the senses. Through synaptic connections neurons interact and create complex dynamical states in response to these inputs. Networks with different connectivity patterns are thought to display different states and therefore subserve different computational goals. In this thesis, we mainly study brain rhythms, a dynamical state that occurs in various neural structures. Rhythms are emergent oscillations that typically occur in homogeneous recurrent networks, whose neurons have identical properties and are densely interconnected. Many sensory systems comprise neurons with opposite ON and OFF responses to inputs. We show that homogenous recurrent networks fail to sustain rhythms when ON and OFF neurons are present in equal proportions. This happens even when the network is subjected to spatially correlated inputs, which are known to promote synchronized oscillations. In this context, we adapted the so-called linear response theory to include networks containing ON and OFF neurons with different intrinsic properties. In this asymmetric case, oscillations can be recovered. A simpler approach is to segregate the ON and OFF populations, thus producing two oscillating subnetworks. The dynamics of purely feedforward networks are studied next. These networks are composed of two or more populations. The populations are connected in a serial fashion, but neurons are unconnected within the populations. This connectivity scheme is drastically different from the fully recurrent network. Yet, this network is shown to display oscillatorylike properties when subjected to spatially correlated stimulation under certain conditions. We also find that this network can implement various types of gain control, depending on the noise in the system and the strength of synaptic interactions. These results establish some unexpected links between feedforward and recurrent networks. Along the way, we apply our results and conclusions to a well-characterized sensory network, the electrosensory system of weakly electric fish.

Many social, biological and technological systems can be viewed as complex networks with a large number of interacting components. However despite recent advancements in network theory, a satisfactory description of dynamic processes arising in such cooperative systems is a subject of ongoing research. In this dissertation the emergence of dynamical complexity in networks of interacting stochastic oscillators is investigated. In particular I demonstrate that networks of two and three state stochastic oscillators present a second-order phase transition with respect to the strength of coupling between individual units. I show that at the critical point fluctuations of the global order parameter are characterized by an inverse-power law distribution and I assess their renewal properties. Additionally, I study the effect that different types of perturbation have on dynamical properties of the model. I discuss the relevance of those observations for the transmission of information between complex systems.

No description available.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第23002号 / 理博第4679号 / 新制||理||1671(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 大西 明, 准教授 菅沼 秀夫, 教授 田中 貴浩 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

<p> Block copolymers are a class of soft materials which can self-assemble into a variety of ordered structures. One method to induce new structures is the application of an external field such as an electric field. Previously, studies of the field-induced phase transitions are based on the assumption that the structural change follows certain symmetry pattern or simply using real-space numerical methods. The goal of the current project is to develop a simple analytic method to predict the structural change. Our approach is based on a linear response theory, in which the external field is taken as a perturbation and the lowest-order contribution to the solution is computed. We applied our method to the Landau-Brazovskii theory which is valid close to the order-disorder transition point of diblock copolymers. The result shows that there will be an additional term to the order parameter as a response to the external field. The structural change can be predicted by a new Fourier expansion of the order parameter. As an example, we examined the structural change of a body-centered cubic phase under an applied electric field.</p> / Thesis / Master of Science (MSc)

Conventional therapeutic techniques treat the patient by delivering a biotherapeutic to the entire body rather than the target tissue. In the case of chemotherapy, the biotherapeutic is a drug that kills healthy and diseased cells indiscriminately which can lead to undesirable side effects. With targeted delivery, biotherapeutics can be delivered directly to the diseased tissue significantly reducing exposure to otherwise healthy tissue. Typical composite delivery devices are minimally composed of a stimuli responsive polymer, such as poly(N-isopropylacrylamide), allowing for triggered release when heated beyond approximately 32 °C, and magnetic nanoparticles which enable targeting as well as provide a mechanism for stimulus upon alternating magnetic field heating. Although more traditional methods, such as emulsion polymerization, have been used to realize these composite devices, the synthesis is problematic. Poisonous surfactants that are necessary to prevent agglomeration must be removed from the finished polymer, increasing the time and cost of the process. This study seeks to further explore non-toxic, biocompatible, non-residual, photochemical methods of creating stimuli responsive nanogels to advance the targeted biotherapeutic delivery field. Ultraviolet photopolymerization promises to be more efficient, while ensuring safety by using only biocompatible substances. The reactants selected for nanogel fabrication were N-isopropylacrylamide as monomer, methylene bisacrylamide as cross-linker, and Irgacure 2959 as ultraviolet photo-initiator. The superparamagnetic nanoparticles for encapsulation were approximately 10 nm in diameter and composed of magnetite to enable remote delivery and enhanced triggered release properties. Early investigations into the interactions of the polymer and nanoparticles employ a pioneering experimental setup, which allows for coincident turbidimetry and alternating magnetic field heating of an aqueous solution containing both materials. Herein, a low-cost, scalable, and rapid, custom ultraviolet photo-reactor with in-situ, spectroscopic monitoring system is used to observe the synthesis as the sample undergoes photopolymerization. This method also allows in-situ encapsulation of the magnetic nanoparticles simplifying the process. Size characterization of the resulting nanogels was performed by Transmission Electron Microscopy revealing size-tunable nanogel spheres between 50 and 800 nm by varying the ratio and concentration of the reactants. Nano-Tracking Analysis indicates that the nanogels exhibit minimal agglomeration as well as provides a temperature-dependent particle size distribution. Optical characterization utilized Fourier Transform Infrared and Ultraviolet Spectroscopy to confirm successful polymerization. When samples of the nanogels encapsulating magnetic nanoparticles were subjected to an alternating magnetic field a temperature increase was observed indicating that triggered release is possible. Furthermore, a model, based on linear response theory that innovatively utilizes size distribution data, is presented to explain alternating magnetic field heating results. The results presented here will advance targeted biotherapeutic delivery and have a wide range of applications in medical sciences like oncology, gene delivery, cardiology and endocrinology.

Der erste Teil dieser Arbeit beschäftigt sich mit der Frage, welchen Einfluss kurzzeitige Schwankungen der erneuerbaren Energiequellen auf die synchrone Netzfrequenz haben. Zu diesem Zweck wird eine lineare Antworttheorie für stochastische Störungen von dynamischen Systemen auf Netzwerken hergeleitet. Anschließend wird diese Theorie verwendet, um den Einfluss von kurzfristigen Wind- und Sonnenschwankungen auf die Netzdynamik zu analysieren. Hierbei wird gezeigt, dass die Frequenzantwort des Netzes weitestgehend homogen ist, aber die Anfälligkeit für Leistungsschwankungen aufgrund von Leitungsverlusten entlang des Leistungsflusses zunimmt. Der zweite Teil der Arbeit befasst sich mit der Modellierung von netzbildenden Wechselrichterregelungen. Bislang existiert kein universelles Modell zur Beschreibung der kollektiven Dynamik solcher Systeme. Um dies zu erreichen, wird unter Ausnutzung der inhärenten Symmetrie des synchronen Betriebszustandes eine Normalform für netzbildende Akteure abgeleitet. Anschließend wird gezeigt, dass dieses Modell eine gute Annäherung an typische Wechselrichter-Dynamiken bietet, aber auch für eine datengesteuerte Modellierung gut geeignet ist. Der letzte Teil der Arbeit befasst sich mit der Analyse des Risikos von Stromausfällen, welche durch Hurrikans verursacht werden. Hohe Windgeschwindigkeiten verursachen häufig Schäden an der Übertragungsinfrastruktur, welche wiederum zu Überlastungen anderer Komponenten führen und damit eine Kaskade von Ausfällen im gesamten Netz auslösen können. Simulationen solcher Szenarien werden durch die Kombination eines meteorologischen Windmodells sowie eines Modells für kaskadierende Leitungsausfälle durchgeführt. Durch Monte-Carlo-Simulationen in einer synthetischen Nachbildung des texanischen Übertragungsnetzes können einzelne kritische Leitungen identifiziert werden, welche zu großflächigen Stromausfällen führen. / The first part of this thesis addresses the question which impact short-term renewable fluctuations have on the synchronous grid frequency. For this purpose, a linear response theory for stochastic perturbations of networked dynamical systems is derived. This theory is then used to analyze the impact of short-term wind and solar fluctuations on the grid frequency. It is shown that while the network frequency response is mainly homogenous, the susceptibility to power fluctuations is increasing along the power flow due to transmission line losses. The second part of the thesis is concerned with modeling grid-forming inverter controls. So far there exists no universal model for studying the collective dynamics of such systems. By utilizing the inherent symmetry of the synchronous operating state, a normal form for grid-forming actors is derived. It is shown that this model provides a useful approximation of certain inverter control dynamics but is also well-suited for a data-driven modeling approach. The last part of the thesis deals with analyzing the risk of hurricane-induced power outages. High wind speeds often cause damage to transmission infrastructure which can lead to overloads of other components and thereby induce a cascade of failures spreading through the entire grid. Simulations of such scenarios are implemented by combining a meteorological wind field model with a model for cascading line failures. Using Monte Carlo simulations in a synthetic test case resembling the Texas transmission system, it is possible to identify critical lines that trigger large-scale power outages.

In this doctoral thesis, we theoretically investigate the propagation of sound waves in dilute Bose gases, in both the collisionless and hydrodynamic regimes. The study of sound wave is a topic of high relevance for the understanding of dynamical properties of any fluid, classical or quantum, and further provides insightful information about the equation of state of the system. In our work, we focus in particular on the two-dimensional (2D) Bose gas, in which the sound wave is predicted to give useful information about the nature of the superfluid phase transition. Recently, experimental measurement of sound wave in a uniform 2D Bose gas has become available, and we show that the measured data are quantitatively well explained by our collisionless theory. Finally, we study the mixtures of weakly interacting Bose gases, by developing a beyond mean-field theory, which includes the effects of thermal and quantum fluctuations in both the density and spin channels. Our new theory allows for the investigation of sound dynamics, as well as the fundamental problem of phase- separation.

Tous les systèmes biologiques sont formés d’atomes et de molécules qui interagissent et dont émergent des propriétés subtiles et complexes. Par ces interactions, les organismes vivants peuvent subvenir à toutes leurs fonctions vitales. Ces propriétés apparaissent dans tous les systèmes biologiques à des niveaux différents, du niveau des molécules et gènes jusqu’aux niveau des cellules et tissus. Ces dernières années, les physiciens se sont impliqués dans la compréhension de ces aspects particulièrement intrigants, en particulier en étudiant les systèmes vivants dans le cadre de la théorie des réseaux, théorie qui offre des outils d’analyse très puissants. Il est possible aujourd’hui d’identifier deux classes d’approches qui sont utilisée pour étudier ces types de systèmes complexes : les méthodes directes de modélisation et les approches inverses d’inférence. Dans cette thèse, mon travail est basé sur les deux types d’approches appliquées à trois niveaux de systèmes biologiques. Dans la première partie de la thèse, je me concentre sur les premières étapes du développement des tissus biologiques des plantes. Je propose un nouveau modèle pour comprendre la dynamique collective des transporteurs de l’hormone auxine et qui permet la croissance non-homogène des tissu dans l’espace et le temps. Dans la deuxième partie de la thèse, j’analyse comment l’évolution contraint la diversité́ de séquence des protéines tout en conservant leur fonction dans différents organismes. En particulier, je propose une nouvelle méthode pour inférer les sites essentiels pour la fonction ou la structure de protéines à partir d’un ensemble de séquences biologiques. Finalement, dans la troisième partie de la thèse, je travaille au niveau cellulaire et étudie les réseaux de signalisation associés à l’auxine. Dans ce contexte, je reformule un modèle préexistant et propose une nouvelle technique qui permet de définir et d’étudier la réponse du système aux signaux externes pour des topologies de réseaux différentes. J’exploite ce cadre théorique pour identifier le rôle fonctionnel de différentes topologies dans ces systèmes. / All biological systems are made of atoms and molecules interacting in a non- trivial manner. Such non-trivial interactions induce complex behaviours allow- ing organisms to fulfill all their vital functions. These features can be found in all biological systems at different levels, from molecules and genes up to cells and tissues. In the past few decades, physicists have been paying much attention to these intriguing aspects by framing them in network approaches for which a number of theoretical methods offer many powerful ways to tackle systemic problems. At least two different ways of approaching these challenges may be considered: direct modeling methods and approaches based on inverse methods. In the context of this thesis, we made use of both methods to study three different problems occurring on three different biological scales. In the first part of the thesis, we mainly deal with the very early stages of tissue development in plants. We propose a model aimed at understanding which features drive the spontaneous collective behaviour in space and time of PINs, the transporters which pump the phytohormone auxin out of cells. In the second part of the thesis, we focus instead on the structural properties of proteins. In particular we ask how conservation of protein function across different organ- isms constrains the evolution of protein sequences and their diversity. Hereby we propose a new method to extract the sequence positions most relevant for protein function. Finally, in the third part, we study intracellular molecular networks that implement auxin signaling in plants. In this context, and using extensions of a previously published model, we examine how network structure affects network function. The comparison of different network topologies provides insights into the role of different modules and of a negative feedback loop in particular. Our introduction of the dynamical response function allows us to characterize the systemic properties of the auxin signaling when external stimuli are applied.