<p>The single particle model was extended to include electrode and particle volume expansion effects observed in high capacity alloying electrodes. The model was used to predict voltage profiles in sodium ion batteries with tin and tin-phosphide negative electrodes. It was seen that the profiles predicted by the modified model were significantly better than the classical model. A parametric study was done to understand the impact of properties such as particle radius, diffusivity, reaction rate etc on the performance of the electrode. The model was also modified for incorporating particles having a cylindrical morphology. For the same material properties, it was seen that cylindrical particles outperform spherical particles for large L/R values in the cylinder due to the diffusion limitations at low L/R ratios. A lattice spring-based degradation model was used to observe crack formation and creep relaxation within the particle. It was observed that the fraction of broken bonds increases with an increase in strain rate. At low strain rates, it was seen that there was a significant expansion in particle volumes due to creep deformation. This expansion helped release particle stresses subsequently reducing the amount of fracture.</p>

We present a method to extract and visualize vortices that originate from bounding walls of three-dimensional time- dependent flows. These vortices can be detected using their footprint on the boundary, which consists of critical points in the wall shear stress vector field. In order to follow these critical points and detect their transformations, affected regions of the surface are parameterized. Thus, an existing singularity tracking algorithm devised for planar settings can be applied. The trajectories of the singularities are used as a basis for seeding particles. This leads to a new type of streak line visualization, in which particles are released from a moving source. These generalized streak lines visualize the particles that are ejected from the wall. We demonstrate the usefulness of our method on several transient fluid flow datasets from computational fluid dynamics simulations.

The prognosis of cancer patients suffering from solid tumors significantly depends on the developmental stage of the tumor. For cervix carcinoma the prognosis is better for compact shapes than for diffusive shapes since the latter may already indicate invasion, the stage in tumor progression that precedes the formation of metastases. In this paper, we present methods for describing and evaluating tumor objects and their surfaces based on topological and geometric properties. For geometry, statistics of the binary object's distance transform are used to evaluate the tumor's invasion front. In addition, a simple compactness measure is adapted to 3D images and presented to compare different types of tumor samples. As a topological measure, the Betti numbers are calculated of voxelized tumor objects based on a medial axis transform. We further illustrate how these geometric and topological properties can be used for a quantitative comparison of histological material and single-cell-based tumor growth simulations.

Obesity is one of the major health concerns nowadays according to the World Health Organisation (WHO global status report on noncommunicable diseases 2010). Thus, there is an urgent need for understanding obesity-associated alterations in food-related and general cognition and their underlying structural and functional correlates within the central nervous system (CNS). Neuroscientific research of the past decade has mainly focussed on obesity-related differences within homeostatic and hedonic processing of food stimuli. Therein, alterations during anticipation and consumption of food-reward stimuli in obese compared with lean subjects have been highlighted. This points at an altered adaptation of eating behavior in obese individuals. This thesis investigates if adaptation of behavior is attenuated in obese compared to lean individuals in learning-related processes beyond the food domain. In five consecutive experimental studies, we show that obese participants reveal reduced adaptation of behavior within and outside the food context. With the help of MRI, we relate these behavioral findings to alterations in structure and function of the fronto-striatal dopaminergic system in obesity. In more detail, reduced behavioral adaptation seems to be associated with attenuated utilization of negative prediction errors in obese individuals. Within the brain, this relates to reduced functional coupling between subcortical dopaminergic target regions (ventral striatum) and executive cortical structures (supplementary motor area) in obesity, as revealed by fMRI analysis.

Collective robotic behavior poses significant advantages over classical control methods such as system response and robustness. Biological cooperative communities have provided great insights for development of many control algorithms. Localized chemical signaling within bacterial communities is used for directed movement and dynamic density measurements. Both individual and population scale models have been created to adequately model community dynamics. These dynamics, including directed motion due to chemotaxis and density controlled functionality from quorum sensing, are modeled through an individual scale in a community scale environment. This modeling provides both a platform for analyzing the BacteriaBot engineered system as well as inspires decentralized stochastic control techniques for solving bacteria-like collaborative control problems. / Master of Science

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Thesis advisor: Babak Momeni / Diseases of the upper respiratory tract encompass a plethora of complex multifaceted etiologies ranging from acute viral and bacterial infections to chronic diseases of the lung and nasal cavity. Due to this inherent complexity, typical treatments often fail in the face of recalcitrant infections and/or severe forms of chronic disease, including asthma. Thus, in order to provide improved standard of care, the mechanisms at play in hard-to-treat etiologies must be better understood. More recently, research has demonstrated a significant association between microbiota and many URT diseases. Previous work has also identified species capable of directly inhibiting standard treatments used to control asthma exacerbations. Despite an exhaustive collection of data characterizing microbiota composition in states of both health and disease, our knowledge of what microbiota profiles are observed in what specific disease etiologies is severely lacking. Yet, gaining these insights is crucial for the translation of such data into application. In this thesis I sought to: 1) identify gut microbiota profiles associated with severe and treatment resistant forms of childhood asthma, and 2) formulate a predictive model to facilitate the restructuring of microbiota for desired therapeutic outcomes. To identify gut microbiota and metabolites enriched in severe and treatment resistant childhood asthma, I looked to an ongoing longitudinal human study on vitamin D and childhood asthma. In this study, I find several fecal bacterial taxa and metabolites associated with more severe (i.e., higher wheeze proportion) and treatment resistant asthma in children at age 3 years. Specifically, several Veillonella species were enriched in children with higher wheeze proportion and in children that responded poorly to inhaled corticosteroid treatment (ICS) (i.e., non-responders). Haemophilus parainfluenzae, a species previously identified as enriched in the airway of adults with ICS-resistant asthma, was also uniquely enriched in children considered ICS non-responders in this study. Several metabolic pathways were also distinctly enriched: histidine metabolism was enriched in children with higher wheeze proportion while sphingolipid metabolism was enriched in ICS non-responders. Both metabolic pathways have been previously identified in association with asthma, further corroborating their role in this disease. Yet, this study is the first to identify these taxa and metabolites in children with preexisting and treatment resistant asthma. In the pursuit of improved treatment outcomes for recalcitrant URT diseases, recent efforts have turned towards microbiota-based therapies. While such treatments have proven successful in the treatment of gastrointestinal infections, these methods have not yet been extended to other conditions. Considering this, I ask whether a predictive model describing microbial interactions can facilitate the restructuring of microbiota for desired therapeutic outcomes. For this, I use a community of nasal microbiota to determine when a simply Lotka-Volterra-like (LV) model is a suitable representation for microbial interactions. I then utilize our LV-like model to examine whether environmental fluctuations have a major influence on community assembly and composition. For this, I looked specifically at pH fluctuations. In this study, I found that LV-like models are most suitable for describing community dynamics in complex low nutrient conditions. I also identified simple in vitro experiments that can reliably predict the suitability of a LV-like model for describing outcomes of a two-species community. When our LV-like model was applied to an in silico community of nasal species to determine the impact of environmental fluctuations, I find that nasal communities are generally robust against pH fluctuations and that, in this condition, facilitative interactions are a stabilizing force, and thus, selected for in in silico enrichment experiments. Overall, this thesis further corroborates the association of microbiota with URT diseases and treatment outcomes while also providing unique insight into their association with specific etiologies in childhood asthma. This thesis also provides a framework for developing models able to facilitate the development of future microbiota-based therapies while also determining how, and when, environmental factors impact community assembly and composition. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

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