NIBP is a prototype member of a novel protein family. It forms a novel subcomplex of NIK-NIBP-IKKB and enhances cytokine-induced IKKB-mediated NFKB activation. It is also named TRAPPC9 as a key member of trafficking particle protein (TRAPP) complex II, which is essential in trans-Golgi networking (TGN). The signaling pathways and molecular mechanisms for NIBP actions remain largely unknown. The aim of this research is to identify potential proteins interacting with NIBP, resulting in the regulation of NFKB signaling pathways and other unknown signaling pathways. At the laboratory of Dr. Wenhui Hu in the Department of Neuroscience, Temple University, sixteen partner proteins were experimentally identified that potentially bind to NIBP. NIBP is a novel protein with no entry in the Protein Data Bank. From a computational and bioinformatics standpoint, we use prediction of secondary structure and protein disorder as well as homology-based structural modeling approaches to create a hypothesis on protein-protein interaction between NIBP and the partner proteins. Structurally, NIBP contains three distinct regions. The first region, consisting of 200 amino acids, forms a hybrid helix and beta sheet-based domain possibly similar to Sybindin domain. The second region comprised of approximately 310 residues, forms a tetratrico peptide repeat (TPR) zone. The third region is a 675 residue long all beta sheet and loops zone with as many as 35 strands and only 2 helices, shared by Gryzun-domain containing proteins. It is likely to form two or three beta sheet sandwiches. The TPR regions of many proteins tend to bind to the peptides from disordered regions of other proteins. Many of the 16 potential binding proteins have high levels of disorder. These data suggest that the TPR region in NIBP most likely binds with many of these 16 proteins through peptides and other domains. It is also possible that the Sybindin-like domain and the Gryzun-like domain containing beta sheet sandwiches bind to some of these proteins. / Bioengineering

Yes / Real time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taking into consideration both spatial (road links) and temporal (lag or past traffic flow values) information. We propose a Layered Ensemble Model (LEM) which combines Artificial Neural Networks and Graded Possibilistic Clustering obtaining an accurate forecast of the traffic flow rates with outlier detection. Experimentation has been carried out on two different data sets. The former was obtained from real UK motorway and the later was obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed LEM model for short-term traffic forecasting provides promising results and given the ability for outlier detection, accuracy, robustness of the proposed approach, it can be fruitful integrated in traffic flow management systems.

The objective of this work was to design, analyze and test a fast response fiber-optic temperature probe and sensor. The sensor is intended for measuring rapid temperature changes such as produced by a blast wave formed by a detonation. This work was performed in coordination with Luna Innovations Incorporated, and the design is based on extensions of an existing fiber-optic temperature sensor developed by Luna. The sensor consists of a glass fiber with an optical wafer attached to the tip. A basic description of the principles behind the fiber-optic temperature sensor and an accompanying demodulation system is provided. For experimental validation tests, shock tubes were used to simulate the blast wave experienced at a distance of 3.0 m from the detonation of 22.7 kg of TNT. The flow conditions were predicted using idealized shock tube theory. The temperature sensors were tested in three configurations, flush at the end of the shock tube, extended on a probe 2.54 cm into the flow and extended on a probe 12.7 cm into the flow. The total temperature was expected to change from 300 K to 1130 K for the flush wall experiments and from 300 K to 960 K for the probe experiments. During the initial 0.1 milliseconds of the data the temperature only changed 8 K when the sensors were flush in the end of the shock tube. The sensor temperature changed 36 K during the same time when mounted on a probe in the flow. Schlieren pictures were taken of the flow in the shock tube to further understand the shock tube environment. Contrary to ideal shock tube theory, it was discovered that the flow did not remain stagnant in the end of the shock tube after the shock reflects from the end of the shock tube. Instead, the effects of turbulence were recorded with the fiber-optic sensors, and this turbulence was also captured in the schlieren photographs. A fast-response thermocouple was used to collect data for comparison with the fiber-optic sensor, and the fiber-optic sensor was proven to have a faster response time compared to the thermocouple. When the sensors were extended 12.7 cm into the flow, the fiber-optic sensors recorded a temperature change of 143 K compared to 38 K recorded by the thermocouple during the 0.5 millisecond test. This corresponds to 22% of the change of total temperature in the air recorded by the fiber-optic sensor and only 6% recorded by the thermocouple. Put another way, the fiber-optic sensor experience a rate of temperature change equal to 2.9x105 K/s and the thermocouple changed at a rate of 0.79x105 K/s. The data recorded from the fiber-optic sensor also contained much less noise than the thermocouple data. An unsteady finite element thermal model was created using ANSYS to predict the temperature response of the sensor. Test cases with known analytical solutions were used to verify the ANSYS modeling procedures. The shock tube flow environment was also modeled with Fluent, a commercially available CFD code. Fluent was used to determine the heat transfer between the shock tube flow and the sensor. The convection film coefficient for the flow was predicted by Fluent to be 27,150 W/m2K for the front of the wafer and 13,385 W/m2K for the side. The Fluent results were used with the ANSYS model to predict the response of the fiber-optic sensor when exposed to the shock tube flow. The results from the Fluent/ANSYS model were compared to the fiber-optic measurements taken in the shock tube. It was seen that the heat flux to the sensor was slightly over-predicted by the model, and the heat losses from the wafer were also over-predicted. Since the prediction fell within the uncertainty of the measurement, it was found to be in good agreement with the measured values. Inverse heat transfer methods were used to determine the total temperature of the flow from the measured data. Both the total temperature and the film coefficient were determined simultaneously during this process. It was found that for short testing times, there were many possible solutions. In order to obtain ultimate success with this method, the uncertainty of the demodulation system must be improved and/or the simple analytical thermal model used to predict the response of the sensor needs to match the physical sensor. Whenever possible, longer testing times should be employed. Promising suggestions for extending this approach are provided. / Ph. D.

Risky decision-making and social influence are associated with many health-risk behaviors. However, more work is necessary to understand risky decision-making and social influence. Additionally, to begin identifying ways to change individuals' engagement in health-risk behaviors, more work is necessary to understand whether and how risky decision-making and social influence can be modulated. Using computational modeling in conjunction with other techniques, this dissertation 1) explores mechanisms underlying risky decision-making under social influence (Study 1) and 2) examines how individuals could modulate risky decision-making and social influence (Studies 2 and 3). Study 1 identifies a novel social heuristic decision-making process whereby individuals who are more uncertain about risky decisions follow others and proposes dorsolateral prefrontal cortex (dlPFC) as a 'controller' of this heuristic. Study 2 finds that giving individuals agency in viewing social information increases the utility of that information. Study 3 finds that some individuals can modulate brain patterns associated with risky decision-making using a real-time fMRI (rt-fMRI) neurofeedback paradigm, and preliminarily shows that this leads to behavior change in risky decision-making. In sum, these studies expand on previous work elucidating mechanisms of risky decision-making under social influence and suggest two possible avenues (agency and real-time fMRI neurofeedback) by which individuals can be taught to change their behavior when making risky decisions under social influence. / Doctor of Philosophy / Risky decision-making and social influence are associated with many health-risk behaviors such as smoking and alcohol use. However, more work is necessary to understand risky decision-making and social influence. Additionally, to identify ways to change individuals' engagement in health-risk behaviors, more work is necessary to understand how risky decision-making and social influence can be changed. Here, computational modeling, a way to quantify individual's behavior, is used in a series of studies to 1) understand how individuals make risky decisions under social influence (Study 1) and 2) test ways in which individuals can be guided to change the way they respond to social influence (Study 2) and make risky decisions (Study 3). Study 1 shows that individuals who do not have strong preferences respond to social information in a different way than those who do and utilizes neuroimaging to identify a particular brain region which may be responsible for this process. Study 2 shows that individuals are more influenced by others when they ask to see their choices, as compared to passively viewing others' choices. Study 3 shows that a brain–computer interface can be used to guide individuals to change their brain activity related to risky decision-making and preliminarily demonstrates that following this training individuals change their risky decisions. Together, these studies further the field's understanding of how individuals make risky decisions under social influence and suggest avenues for behavior change in risky decision-making under social influence.

During acute and chronic infectious diseases hosts develop complex immune responses to cope with bacterial persistence. Depending on a variety of host and microbe factors, outcomes range from peaceful co-existence to detrimental disease. Mechanisms underlying immunity to bacterial stimuli span several spatiotemporal magnitudes and the summation of these hierarchical interactions plays a decisive role in pathogenic versus tolerogenic fate for the host. This dissertation integrates diverse data from immunoinformatics analyses, experimental validation and mathematical modeling to investigate a series of hypotheses driven by computational modeling to study mucosal immunity. Two contrasting microbes, enteroaggregative Escherichia coli and Helicobacter pylori, are used to perturb gut immunity in order to discover host-centric targets for modulating the host immune system. These findings have the potential to be broadly applicable to other infectious and immune-mediated diseases and could assist in the development of antibiotic-free and host-targeted treatments that modulate tolerance to prevent disease. / Ph. D.

Major depressive disorder is a common, impairing disease, but current treatments are only moderately effective. Understanding how processes such as reward and punishment learning are disrupted in depression and how these disruptions are remediated through treatment is vital to improving outcomes for people with this disorder. In the present set of studies, computational reinforcement learning models and neuroimaging were used to understand how symptom clusters of depression (anhedonia and negative affect) were related to neural and behavioral measures of learning (Study 1, in Paper 1), how these alterations changed with improvement in symptoms after cognitive behavioral therapy (Study 2, in Paper 1), and how learning parameters could be directly altered in a learning retraining paradigm (Study 3, in Paper 2). Results showed that anhedonia and negative affect were uniquely related to changes in learning and that improvement in these symptoms correlated with changes in learning parameters; these parameters could also be changed through targeted queries based on reinforcement learning theory. These findings add important information to how learning is disrupted in depression and how current and novel treatments can remediate learning and improve symptoms. / Ph. D.

Elastic Buckling Simplified Methods for Structural Members with Edge-Stiffened Holes: Presently, the current design methods available to engineers to predict the strength of cold-formed steel members with edge-stiffened holes remains largely unaddressed in the North American Specification for the Design of Cold-Formed Steel Structural Members (NAS). Research was conducted to explore and develop a further understanding of the effects of stiffened edge holes on the elastic buckling parameters for local, distortional, and global buckling. Elastic buckling parameter studies have been conducted on a suite of cold-formed members including recently developed DeltaSTUDs manufactured by Steelform Building Products, Inc. and a series of common Steel Stud Manufacturers Association (SSMA) members. Furthermore, a suite of simplified methods for determining elastic buckling parameters used to predict capacity with the Direct Strength Method (DSM) for members with edge stiffened holes were developed and validated. The elastic buckling studies are used to validate the simplified methods presented in this thesis. All simplified methods are further validated with thin shell finite element eigen-buckling parameter studies where the edge-stiffened holes are explicitly modeled. Purlin Distortional Buckling Strength Under Gravity Loading: Laterally braced cold-formed steel beams generally fail due to local and/or distortional buckling in combination with yielding. For many members, distortional buckling is the dominant buckling mode and is addressed in the current North American Specification for the Design of Cold-formed Steel Structural Members. The current main code equation, AISI C3.1.4-10 for calculating the available distortional buckling stress was derived experimentally based on a series of four-point bending tests at John Hopkins University. Where this provides a good basis for determining capacity, in most loading conditions purlins are subjected to a downward uniform loading that provides additional resistance to distortional buckling in the top flange beyond the resistance of the steel roofing panel. This research describes an experimental study to explore and quantify the difference in distortional buckling flexural capacity of metal building Z-purlins treated as isolated components and Z-purlins loaded with a constant pressure applied to metal roof panels. A series of three different types of tests have been developed to quantify the system effect provided by the metal roof panels as well as downward pressure on distortional buckling. Results are also extended to validate the Direct Strength Method when predicting flexural capacity of purlins in a roof system. / Master of Science

The cell cycle of eukaryotes consists of alternation between growth and DNA replication (interphase), and DNA distribution and cell-division (mitosis or M-phase). This process is regulated by a complex network of biochemical reactions. A core part of this network, called the "Cell Cycle engine" is evolutionarily conserved. The dimer of CDK1 (a protein kinase) and Cyclin proteins (the regulatory components), called M-phase Promoting Factor (MPF), and its key regulatory proteins Cdc25 and Wee1, are central parts of this cell cycle engine. Maintaining the fidelity of the DNA during the cell cycle is critical for successful propagation of the cell lineage. In the presence of unreplicated DNA, the cell cycle engine''s progress into mitosis is slowed down (or halted) by regulation of MPF activity through Cdc25 and Wee1. This regulatory event, called the unreplicated DNA checkpoint, was modeled in a rudimentary fashion in the Novak and Tyson (1993) model of frog eggs. Since then, many new experiments have uncovered relevant parts of this network. Here, we include these parts into a detailed model of the unreplicated DNA checkpoint in the cell cycle of frog-egg extracts. This work and future studies of the unreplicated DNA checkpoint will lead to its better understanding and hopefully to some strategies for tackling cancer. / Master of Science

<p>Neuronal current MRI (ncMRI) is a field of study to directly map electrical activity in the brain using MRI, which has many benefits over functional MRI. One potential ncMRI method, Lorentz effect imaging (LEI), has shown promise but needs a better theoretical understanding to improve its use.</p><p>We develop three computational models to simulate the LEI experiments of an electrolyte filled phantom subject to a current dipole based on: ion flow, particle drift, and electromagnetohydrodynamics (EMHD). With comparative experimental results, we use the EMHD model to better understand the Lorentz effect over a range of current strengths. We also quantify the LEI experimental images and assess ways to measure the underlying current strength, which would greatly benefit comparative brain mapping.</p><p>EMHD is a good predictor of LEI signal loss. We can measure the underlying current strength and polarity in the phantom using LEI images. We can also use trends from the EMHD model results to predict the required current density for signal detection in future LEI experiments. We can also infer the electric field strength, flow velocity, displacement, and pressure from the predicted current magnitude in an LEI experiment.</p><p>The EMHD model provides information that greatly improves the utility and understanding of LEI. Future study with our EMHD model should be performed using shorter dipole lengths, higher density and lower strength of current sources, and varying current source frequencies to understand LEI in the setting of mapping brain activity.</p> / Dissertation

Existe atualmente na sociedade um grande número de sistemas reais de alta complexidade. Esta complexidade pode ser definida tanto do ponto de vista da dificuldade em identificar todas as partes que compõem estes sistemas, como também, pela compreensão e definição real da relação entre estas partes, permitindo, desta forma uma representação adequada do comportamento global do sistema. O comportamento global destes sistemas não se caracteriza pela soma do comportamento de suas partes componentes. Normalmente, a modelagem destes sistemas não reflete, de forma realística, o seu comportamento, devido ao excesso de simplificações realizadas. Por outro lado, alguns modelos são impraticáveis de serem aplicados, devido ao excessivo esforço computacional e a restrições de tempo. O presente trabalho de pesquisa apresenta uma proposta de um modelo de otimização para um problema real de alta complexidade e com fortes requisitos de segurança (safety) encontrado na Infra-estrutura Aeronáutica Brasileira e Mundial. Este problema está relacionado ao desbalanceamento entre a capacidade e demanda em infra-estrutura aeronáutica em sistemas de transporte aéreo. Para tanto, o trabalho propõe um Modelo de Otimização de Demanda (MOD) em infra-estrutura aeronáutica, através da técnica de Inteligência Artificial denominada de Algoritmos Genéticos. A pesquisa analisa a eficiência do modelo proposto em termos da resolução do problema, bem como quanto à qualidade das respostas apresentadas. De forma complementar é avaliada a importância de cada um dos parâmetros do modelo de otimização através da sua flexibilização. / Nowadays, in the society, there are a great number of real systems with high complexity. This complexity can be justified in function of the difficulty in identifying all parts that compose these systems, but also the complex relationship between them. The global behavior of these systems is not characterized for the addition of the behavior of its contracting parties. Normally, the modeling of these systems does not reflect its realistic behavior, due the excess of simplifications carried out. On the other hand, some models are impracticable to be solved, because the extreme computational effort necessary. The present research develops a proposal of an optimization model for a real problem of high complexity and with hard safety requirements found in the Brazilian and world-wide aeronautical infrastructure. This problem deals with the unbalancing between the capacity and demand in infrastructure aeronautics in air transportation systems. The work considers a Demand Optimization Model (DOM) for aeronautical infrastructure through the technique of artificial intelligence denominated Genetic Algorithms. The research analyzes the efficiency of the considered model in terms of problem resolution, as well as, the quality of the presented answers. Of complementary form, some parameters of the model were adjusted and their importance were avaluated.