Neuronal mechanism of state control in Drosophila melanogaster

The changes in internal states, such as fear, hunger and sleep affect behavioral responses in animals. In most of the cases, these state-dependent influences are “pleiotropic”: one state affects multiple sensory modalities and behaviors; “scalable”: the strengths and choices of such modulations differ depending on the imminence of demands; and “persistent”: once the state is switched on the effects last even after the internal demands are off. These prominent features of state-control enable animals to adjust their behavioral responses depending on their internal demands. Here, we studied the neuronal mechanisms of state-controls by investigating energy-deprived state (hunger state) and social-deprived state of fruit flies, Drosophila melanogaster, as prototypic models. To approach these questions, we developed two novel methods: a genetically based method to map sites of neuromodulation in the brain and optogenetic tools in Drosophila.

These methods, and genetic perturbations, reveal that the effect of hunger to alter behavioral sensitivity to gustatory cues is mediate by two distinct neuromodulatory pathways. The neuropeptide F (NPF) – dopamine (DA) pathway increases sugar sensitivity under mild starvation, while the adipokinetic hormone (AKH)- short neuropeptide F (sNPF) pathway decreases bitter sensitivity under severe starvation. These two pathways are recruited under different levels of energy demands without any cross interaction. Effects of both of the pathways are mediated by modulation of the gustatory sensory neurons, which reinforce the concept that sensory neurons constitute an important locus for state-dependent control of behaviors. Our data suggests that multiple independent neuromodulatory pathways are underlying pleiotropic and scalable effects of the hunger state.

In addition, using optogenetic tool, we show that the neural control of male courtship song can be separated into probabilistic/biasing, and deterministic/command-like components. The former, but not the latter, neurons are subject to functional modulation by social experience, supporting the idea that they constitute a locus of state-dependent influence. Interestingly, moreover, brief activation of the former, but not the latter, neurons trigger persistent behavioral response for more than 10 min. Altogether, these findings and new tools described in this dissertation offer new entry points for future researchers to understand the neuronal mechanism of state control.

Efficient methods for stochastic optimal control

The Hamilton Jacobi Bellman (HJB) equation is central to stochastic optimal control (SOC) theory, yielding the optimal solution to general problems specified by known dynamics and a specified cost functional. Given the assumption of quadratic cost on the control input, it is well known that the HJB reduces to a particular partial differential equation (PDE). While powerful, this reduction is not commonly used as the PDE is of second order, is nonlinear, and examples exist where the problem may not have a solution in a classical sense. Furthermore, each state of the system appears as another dimension of the PDE, giving rise to the curse of dimensionality. Since the number of degrees of freedom required to solve the optimal control problem grows exponentially with dimension, the problem becomes intractable for systems with all but modest dimension.

In the last decade researchers have found that under certain, fairly non-restrictive structural assumptions, the HJB may be transformed into a linear PDE, with an interesting analogue in the discretized domain of Markov Decision Processes (MDP). The work presented in this thesis uses the linearity of this particular form of the HJB PDE to push the computational boundaries of stochastic optimal control.

This is done by crafting together previously disjoint lines of research in computation. The first of these is the use of Sum of Squares (SOS) techniques for synthesis of control policies. A candidate polynomial with variable coefficients is proposed as the solution to the stochastic optimal control problem. An SOS relaxation is then taken to the partial differential constraints, leading to a hierarchy of semidefinite relaxations with improving sub-optimality gap. The resulting approximate solutions are shown to be guaranteed over- and under-approximations for the optimal value function. It is shown that these results extend to arbitrary parabolic and elliptic PDEs, yielding a novel method for Uncertainty Quantification (UQ) of systems governed by partial differential constraints. Domain decomposition techniques are also made available, allowing for such problems to be solved via parallelization and low-order polynomials.

The optimization-based SOS technique is then contrasted with the Separated Representation (SR) approach from the applied mathematics community. The technique allows for systems of equations to be solved through a low-rank decomposition that results in algorithms that scale linearly with dimensionality. Its application in stochastic optimal control allows for previously uncomputable problems to be solved quickly, scaling to such complex systems as the Quadcopter and VTOL aircraft. This technique may be combined with the SOS approach, yielding not only a numerical technique, but also an analytical one that allows for entirely new classes of systems to be studied and for stability properties to be guaranteed.

The analysis of the linear HJB is completed by the study of its implications in application. It is shown that the HJB and a popular technique in robotics, the use of navigation functions, sit on opposite ends of a spectrum of optimization problems, upon which tradeoffs may be made in problem complexity. Analytical solutions to the HJB in these settings are available in simplified domains, yielding guidance towards optimality for approximation schemes. Finally, the use of HJB equations in temporal multi-task planning problems is investigated. It is demonstrated that such problems are reducible to a sequence of SOC problems linked via boundary conditions. The linearity of the PDE allows us to pre-compute control policy primitives and then compose them, at essentially zero cost, to satisfy a complex temporal logic specification.

Methods for melting temperature calculation

Melting temperature calculation has important applications in the theoretical study of phase diagrams and computational materials screenings. In this thesis, we present two new methods, i.e., the improved Widom's particle insertion method and the small-cell coexistence method, which we developed in order to capture melting temperatures both accurately and quickly.

We propose a scheme that drastically improves the efficiency of Widom's particle insertion method by efficiently sampling cavities while calculating the integrals providing the chemical potentials of a physical system. This idea enables us to calculate chemical potentials of liquids directly from first-principles without the help of any reference system, which is necessary in the commonly used thermodynamic integration method. As an example, we apply our scheme, combined with the density functional formalism, to the calculation of the chemical potential of liquid copper. The calculated chemical potential is further used to locate the melting temperature. The calculated results closely agree with experiments.

We propose the small-cell coexistence method based on the statistical analysis of small-size coexistence MD simulations. It eliminates the risk of a metastable superheated solid in the fast-heating method, while also significantly reducing the computer cost relative to the traditional large-scale coexistence method. Using empirical potentials, we validate the method and systematically study the finite-size effect on the calculated melting points. The method converges to the exact result in the limit of a large system size. An accuracy within 100 K in melting temperature is usually achieved when the simulation contains more than 100 atoms. DFT examples of Tantalum, high-pressure Sodium, and ionic material NaCl are shown to demonstrate the accuracy and flexibility of the method in its practical applications. The method serves as a promising approach for large-scale automated material screening in which the melting temperature is a design criterion.

We present in detail two examples of refractory materials. First, we demonstrate how key material properties that provide guidance in the design of refractory materials can be accurately determined via ab initio thermodynamic calculations in conjunction with experimental techniques based on synchrotron X-ray diffraction and thermal analysis under laser-heated aerodynamic levitation. The properties considered include melting point, heat of fusion, heat capacity, thermal expansion coefficients, thermal stability, and sublattice disordering, as illustrated in a motivating example of lanthanum zirconate (La₂Zr₂O₇). The close agreement with experiment in the known but structurally complex compound La₂Zr₂O₇ provides good indication that the computation methods described can be used within a computational screening framework to identify novel refractory materials. Second, we report an extensive investigation into the melting temperatures of the Hf-C and Hf-Ta-C systems using ab initio calculations. With melting points above 4000 K, hafnium carbide (HfC) and tantalum carbide (TaC) are among the most refractory binary compounds known to date. Their mixture, with a general formula Ta_xHf_1-xC_y, is known to have a melting point of 4215 K at the composition Ta₄HfC₅, which has long been considered as the highest melting temperature for any solid. Very few measurements of melting point in tantalum and hafnium carbides have been documented, because of the obvious experimental difficulties at extreme temperatures. The investigation lets us identify three major chemical factors that contribute to the high melting temperatures. Based on these three factors, we propose and explore a new class of materials, which, according to our ab initio calculations, may possess even higher melting temperatures than Ta-Hf-C. This example also demonstrates the feasibility of materials screening and discovery via ab initio calculations for the optimization of "higher-level" properties whose determination requires extensive sampling of atomic configuration space.

Relativistic mean field theory: methods and applications

We develop a method for performing one-loop calculations in finite systems that is based on using the WKB approximation for the high energy states. This approximation allows us to absorb all the counterterms analytically and thereby avoids the need for extreme numerical precision that was required by previous methods. In addition, the local approximation makes this method well suited for self-consistent calculations. We then discuss the application of relativistic mean field methods to the atomic nucleus. Self-consistent, one loop calculations in the Walecka model are performed and the role of the vacuum in this model is analyzed. This model predicts that vacuum polarization effects are responsible for up to five percent of the local nucleon density. Within this framework the possible role of strangeness degrees of freedom is studied. We find that strangeness polarization can increase the kaon-nucleus scattering cross section by ten percent. By introducing a cutoff into the model, the dependence of the model on short-distance physics, where its validity is doubtful, is calculated. The model is very sensitive to cutoffs around one GeV.

Análise de unidades de pré-purificação de ar por adsorção

Os principais constituintes do ar, nitrogênio, oxigênio e argônio, estão cada vez mais presentes nas indústrias, onde são empregados nos processos químicos, para o transporte de alimentos e processamento de resíduos. As duas principais tecnologias para a separação dos componentes do ar são a adsorção e a destilação criogênica. Entretanto, para ambos os processos é necessário que os contaminantes do ar, como o gás carbônico, o vapor dágua e hidrocarbonetos, sejam removidos para evitar problemas operacionais e de segurança. Desta forma, o presente trabalho trata do estudo do processo de pré-purificação de ar utilizando adsorção. Neste sistema a corrente de ar flui alternadamente entre dois leitos adsorvedores para produzir ar purificado continuamente. Mais especificamente, o foco da dissertação corresponde à investigação do comportamento de unidades de pré-purificação tipo PSA (pressure swing adsorption), onde a etapa de dessorção é realizada pela redução da pressão. A análise da unidade de pré-purificação parte da modelagem dos leitos de adsorção através de um sistema de equações diferenciais parciais de balanço de massa na corrente gasosa e no leito. Neste modelo, a relação de equilíbrio relativa à adsorção é descrita pela isoterma de Dubinin-Astakhov estendida para misturas multicomponentes. Para a simulação do modelo, as derivadas espaciais são discretizadas via diferenças finitas e o sistema de equações diferenciais ordinárias resultante é resolvido por um solver apropriado (método das linhas). Para a simulação da unidade em operação, este modelo é acoplado a um algoritmo de convergência relativo às quatro etapas do ciclo de operação: adsorção, despressurização, purga e dessorção. O algoritmo em questão deve garantir que as condições finais da última etapa são equivalentes às condições iniciais da primeira etapa (estado estacionário cíclico). Desta forma, a simulação foi implementada na forma de um código computacional baseado no ambiente de programação Scilab (Scilab 5.3.0, 2010), que é um programa de distribuição gratuita. Os algoritmos de simulação de cada etapa individual e do ciclo completo são finalmente utilizados para analisar o comportamento da unidade de pré-purificação, verificando como o seu desempenho é afetado por alterações nas variáveis de projeto ou operacionais. Por exemplo, foi investigado o sistema de carregamento do leito que mostrou que a configuração ideal do leito é de 50% de alumina seguido de 50% de zeólita. Variáveis do processo foram também analisadas, a pressão de adsorção, a vazão de alimentação e o tempo do ciclo de adsorção, mostrando que o aumento da vazão de alimentação leva a perda da especificação que pode ser retomada reduzindo-se o tempo do ciclo de adsorção. Mostrou-se também que uma pressão de adsorção maior leva a uma maior remoção de contaminantes.

The Equation of State and Petrogenesis of Komatiite

(1) Equation of State of Komatiite

The equation of state (EOS) of a molten komatiite (27 wt% MgO) was detennined in the 5 to 36 GPa pressure range via shock wave compression from 1550°C and 0 bar. Shock wave velocity, U_S, and particle velocity, U_P, in km/s follow the linear relationship U_S = 3.13(±0.03) + 1.47(±0.03) U_P. Based on a calculated density at 1550°C, 0 bar of 2.745±0.005 glee, this U_S-U_P relationship gives the isentropic bulk modulus K_S = 27.0 ± 0.6 GPa, and its first and second isentropic pressure derivatives, K'_S = 4.9 ± 0.1 and K"_S = -0.109 ± 0.003 GPa^-1.

The calculated liquidus compression curve agrees within error with the static compression results of Agee and Walker [1988a] to 6 GPa. We detennine that olivine (FO₉₄) will be neutrally buoyant in komatiitic melt of the composition we studied near 8.2 GPa. Clinopyroxene would also be neutrally buoyant near this pressure. Liquidus garnet-majorite may be less dense than this komatiitic liquid in the 20-24 GPa interval, however pyropic-garnet and perovskite phases are denser than this komatiitic liquid in their respective liquidus pressure intervals to 36 GPa. Liquidus perovskite may be neutrally buoyant near 70 GPa.

At 40 GPa, the density of shock-compressed molten komatiite would be approximately equal to the calculated density of an equivalent mixture of dense solid oxide components. This observation supports the model of Rigden et al. [1989] for compressibilities of liquid oxide components. Using their theoretical EOS for liquid forsterite and fayalite, we calculate the densities of a spectrum of melts from basaltic through peridotitic that are related to the experimentally studied komatiitic liquid by addition or subtraction of olivine. At low pressure, olivine fractionation lowers the density of basic magmas, but above 14 GPa this trend is reversed. All of these basic to ultrabasic liquids are predicted to have similar densities at 14 GPa, and this density is approximately equal to the bulk (PREM) mantle. This suggests that melts derived from a peridotitic mantle may be inhibited from ascending from depths greater than 400 km.

The EOS of ultrabasic magmas was used to model adiabatic melting in a peridotitic mantle. If komatiites are formed by >15% partial melting of a peridotitic mantle, then komatiites generated by adiabatic melting come from source regions in the lower transition zone (≈500-670 km) or the lower mantle (>670 km). The great depth of incipient melting implied by this model, and the melt density constraint mentioned above, suggest that komatiitic volcanism may be gravitationally hindered. Although komatiitic magmas are thought to separate from their coexisting crystals at a temperature =200°C greater than that for modern MORBs, their ultimate sources are predicted to be diapirs that, if adiabatically decompressed from initially solid mantle, were more than 700°C hotter than the sources of MORBs and derived from great depth.

We considered the evolution of an initially molten mantle, i.e., a magma ocean. Our model considers the thermal structure of the magma ocean, density constraints on crystal segregation, and approximate phase relationships for a nominally chondritic mantle. Crystallization will begin at the core-mantle boundary. Perovskite buoyancy at > 70 GPa may lead to a compositionally stratified lower mantle with iron-enriched mangesiowiistite content increasing with depth. The upper mantle may be depleted in perovskite components. Olivine neutral buoyancy may lead to the formation of a dunite septum in the upper mantle, partitioning the ocean into upper and lower reservoirs, but this septum must be permeable.

(2) Viscosity Measurement with Shock Waves

We have examined in detail the analytical method for measuring shear viscosity from the decay of perturbations on a corrugated shock front The relevance of initial conditions, finite shock amplitude, bulk viscosity, and the sensitivity of the measurements to the shock boundary conditions are discussed. The validity of the viscous perturbation approach is examined by numerically solving the second-order Navier-Stokes equations. These numerical experiments indicate that shock instabilities may occur even when the Kontorovich-D'yakov stability criteria are satisfied. The experimental results for water at 15 GPa are discussed, and it is suggested that the large effective viscosity determined by this method may reflect the existence of ice VII on the Rayleigh path of the Hugoniot This interpretation reconciles the experimental results with estimates and measurements obtained by other means, and is consistent with the relationship of the Hugoniot with the phase diagram for water. Sound waves are generated at 4.8 MHz at in the water experiments at 15 GPa. The existence of anelastic absorption modes near this frequency would also lead to large effective viscosity estimates.

(3) Equation of State of Molybdenum at 1400°C

Shock compression data to 96 GPa for pure molybdenum, initially heated to 1400°C, are presented. Finite strain analysis of the data gives a bulk modulus at 1400°C, K'_S. of 244±2 GPa and its pressure derivative, K'_OS of 4. A fit of shock velocity to particle velocity gives the coefficients of U_S = C_O+S U_P to be C_O = 4.77±0.06 km/s and S = 1.43±0.05. From the zero pressure sound speed, C_O, a bulk modulus of 232±6 GPa is calculated that is consistent with extrapolation of ultrasonic elasticity measurements. The temperature derivative of the bulk modulus at zero pressure, θK_OSθT|_P, is approximately -0.012 GPa/K. A thermodynamic model is used to show that the thermodynamic Grüneisen parameter is proportional to the density and independent of temperature. The Mie-Grüneisen equation of state adequately describes the high temperature behavior of molybdenum under the present range of shock loading conditions.

The application of methods of ecosystem analysis to the evaluation of water quality

Guided by experience and the theoretical development of hydrobiology, it can be considered that the main aim of water quality control should be the establishment of the rates of the self-purification process of water bodies which are capable of maintaining communities in a state of dynamic balance without changing the integrity of the ecosystem. Hence, general approaches in the elaboration of methods for hydrobiological control are based on the following principles: a. the balance of matter and energy in water bodies; b. the integrity of the ecosystem structure and of its separate components at all levels. Ecosystem analysis makes possible a revelation of the whole totality of factors which determine the anthropogenic evolution of a water body. This is necessary for the study of long-term changes in water bodies. The principles of ecosystem analysis of water bodies, together with the creation of their mathematical models, are important because, in future, with the transition of water demanding production into closed cycles of water supply, changes in water bodies will arise in the main through the influence of 'diffuse' pollution (from the atmosphere, with utilisation in transport etc.).

New neuroimaging methods for clinical neuroscience and neurological disorders

236 p.