The Actin-binding Protein Moesin and Memory Formation in Drosophila : A thesis presented to Massey University in partial fulfillment of the requirements for the degree of Master of Science in Biochemistry

Moesin is a cytoskeletal adaptor protein that plays an important role in modification of the actin cytoskeleton and the formation of dendritic spines, which may be crucial to long-term potentiation. Moesin has also been found to be overexpressed in brains affected by Alzheimer’s disease. Despite being identified as a potential memory gene and linked to several neurological diseases, its role in memory has not been evaluated. The role of Moesin in the Drosophila melanogaster brain was investigated by characterizing the impact of modulating Moesin expression on several aspects of development and behavior. Moesin is involved in both brain and eye development. Knockdown and overexpression of Moesin led to defects in the development of the mushroom body, a brain structure critical for memory formation and recall. Further, knockdown of Moesin throughout development resulted in a significant deficit in long-term memory. Additionally, knockdown of Moesin restricted to adulthood also resulted in a significant deficit in long-term memory, which suggests that Moesin also has a non-developmental role in memory. Further, this requirement for Moesin in long-term memory was traced to the alpha/beta and gamma neurons of the mushroom body. Through the use of a phosphomimetic Moesin mutant that mimics the phosphorylated, activated form of Moesin, the regulation of Moesin in the Drosophila brain was analyzed. Expression of this mutant in neurons disrupted photoreceptor development in the Drosophila eye and a novel sensorimotor phenotype attributed to its expression in the brain was identified resulting in a defect in stereotypical climbing behavior. These results suggest a critical role for Moesin in general neurological functioning and the molecular pathways involved in its activation require further investigation

Use of a Constant Temperature Hot-Wire Anemometer to Compensate for the Thermal Lag

A constant temperature hot-wire anemometer can be used to compensate for the thermal lag of a wire or film resistance thermometer within the useful frequency range of the anemometer. This method may be used as an alternative to the standard method in which a differentiation circuit as employed in constant current anemometers is used for frequency compensation. The new method is useful if a constant temperature anemometer is at hand. The time constant of the resistance thermometer need not be determined

Versatile Inverse Reinforcement Learning via Cumulative Rewards

Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert. Current approaches usually do this with generative and uni-modal models, meaning that they encode a single behavior. In the common setting, where there are various solutions to a problem and the experts show versatile behavior this severely limits the generalization capabilities of these methods. We propose a novel method for Inverse Reinforcement Learning that overcomes these problems by formulating the recovered reward as a sum of iteratively trained discriminators. We show on simulated tasks that our approach is able to recover general, high-quality reward functions and produces policies of the same quality as behavioral cloning approaches designed for versatile behavior

Uranium isotope fractionation during slab dehydration beneath the Izu arc

Fluids released from subducted slabs impart characteristic geochemical signatures on volcanic arc magmas and residual slabs transported into the deeper mantle. Yet, the sources and transport mechanisms of trace elements released from the slab are speculative. We investigate fluids released from subducted slabs from the perspective of 238U/235U and radiogenic Pb isotope ratios in lavas from the Izu volcanic arc in the Pacific ocean. Izu arc lavas are fluid-dominated end-member type magmas that allow a close characterization of slab fluids. The Izu arc lavas have low 238U/235U ratios compared to the bulk Earth and mid-ocean ridge basalt (MORB). The low 238U/235U (δ238U = -0.46 to -0.33 ‰, where δ238U = 238U/235Usample/ 238U/235UCRM145 -1) is associated with slab-derived fluids low in Th/U that are added to the magma sources. The radiogenic Pb isotope ratios of the lavas form an array between ‘Indian’ type MORB and subducting sediments that is inconsistent with fluids derived from the altered mafic oceanic crust (AMOC). We infer that ‘fluid-mobile’ elements, including U and Pb are mobilized from largely unaltered, deeper sections of the mafic crust by migrating fluids that are derived from the dehydration of underlying serpentinites. Uranium is only fluid-mobile as UVI and needs to be oxidised from predominant UIV in unaltered magmatic rocks in order to be mobilised by fluids. Uranium isotope fractionation of ~ 0.2 ‰ in δ238U during this process is required to generate the low 238U/235U in the fluids. We propose that channelized fluid flow through the metamorphosed sheeted dyke and gabbroic sections of the mafic crust locally oxidizes and mobilizes U. We suggest that U isotope fractionation occurs within the fluid channels and is related to equilibrium isotope fractionation during the oxidation of U and the incorporation of UIV into secondary phases such as epidote, apatite and zircon that grow within the channels. These phases are predicted to carry isotopically heavy U into the deeper mantle beyond subduction zones. The δ238U is thus tracing the dehydration process of subducting slabs. Similar observations have been made for other, ‘stable isotope’ systems in different arcs and subduction-related metamorphic rocks, thus highlighting their potential for studying processes occurring within the slabs during subduction. This information is essential for understanding and the partitioning of elements between subducted slabs and the mantle wedge and constraining the role of subduction zones in global geochemical cycles.Leverhulme Trust Early Career Fellowshi

Richard Hausmann : Rede gehalten am Stiftungstage ... d. 18. Januar 1922 zu seinem Gedächtnis

http://www.ester.ee/record=b4260931*es

Proposição de sequenciamento de produção para uma máquina visando a diminuição do tempo total de setups em uma fábrica de chicotes elétricos

Esse artigo tem como propósito pesquisar e auxiliar na elaboração de um algoritmo de sequenciamento de produção para uma máquina de corte e crimpagem que é gargalo de uma célula de uma empresa. A pesquisa é desenvolvida em um Sistema Flexível de Manufatura (FMS – Flexible Manufacturing System) responsável pela pré-montagem de chicotes elétricos com tempos de setup dependentes da sequência de produção. O sequenciamento atual foi elaborado para tentar minimizar as limitações do atual sequenciamento de corte utilizado pela máquina. Dessa forma, foi proposto um algoritmo para minimizar os tempos de setup da máquina. Os resultados obtidos nas simulações do algoritmo foram eficientes, tendo uma redução final de 12,93% de tempo de setup da máquina na amostra realizada, indicando que a forma de sequenciamento proposta foi eficiente e está pronta para ser implementada pela empresa

Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors

Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Thus, they can easily generalize and adapt to new and changing environments. Current Imitation Learning algorithms often only consider unimodal expert demonstrations and act in a state-action-based setting, making it difficult for them to imitate human behavior in case of versatile demonstrations. Instead, we combine a mixture of movement primitives with a distribution matching objective to learn versatile behaviors that match the expert's behavior and versatility. To facilitate generalization to novel task configurations, we do not directly match the agent's and expert's trajectory distributions but rather work with concise geometric descriptors which generalize well to unseen task configurations. We empirically validate our method on various robot tasks using versatile human demonstrations and compare to imitation learning algorithms in a state-action setting as well as a trajectory-based setting. We find that the geometric descriptors greatly help in generalizing to new task configurations and that combining them with our distribution-matching objective is crucial for representing and reproducing versatile behavior.Comment: Accepted as a poster at the 6th Conference on Robot Learning (CoRL), 202

Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors

Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Thus, they can easily generalize and adapt to new and changing environments. Current Imitation Learning algorithms often only consider unimodal expert demonstrations and act in a state-action-based setting, making it difficult for them to imitate human behavior in case of versatile demonstrations. Instead, we combine a mixture of movement primitives with a distribution matching objective to learn versatile behaviors that match the expert's behavior and versatility. To facilitate generalization to novel task configurations, we do not directly match the agent's and expert's trajectory distributions but rather work with concise geometric descriptors which generalize well to unseen task configurations. We empirically validate our method on various robot tasks using versatile human demonstrations and compare to imitation learning algorithms in a state-action setting as well as a trajectory-based setting. We find that the geometric descriptors greatly help in generalizing to new task configurations and that combining them with our distribution-matching objective is crucial for representing and reproducing versatile behavior.Comment: Accepted as a poster at the 6th Conference on Robot Learning (CoRL), 202

Swarm Reinforcement Learning For Adaptive Mesh Refinement

Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on heuristics or expensive error estimators, hindering their use for complex simulations. Recent learning-based AMR methods tackle these issues, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate our approach, Adaptive Swarm Mesh Refinement (ASMR), on challenging refinement tasks. Our approach learns reliable and efficient refinement strategies that can robustly generalize to different domains during inference. Additionally, it achieves a speedup of up to $2$ orders of magnitude compared to uniform refinements in more demanding simulations. We outperform learned baselines and heuristics, achieving a refinement quality that is on par with costly error-based oracle AMR strategies