Isogeometric Analysis for Electromagnetism

The combination of numerical analysis with the scanning technology has been seeing increased use in many research areas. There is an emerging need for high-fidelity geometric modeling and meshing for practical applications. The Isogeometric Analysis (IGA) is a comprehensive computational framework, which integrates geometric modeling and meshing with analysis. Different from other existing numerical methods, the IGA can generate analysis ready models without loss of geometrical accuracy. In IGA, the continuity and the quality of a solution can be conveniently controlled and refined. These features enable IGA to integrate modeling, analysis, and design in a unified framework, the root idea of IGA. The IGA for electromagmetics is studied here for steady and transient electromagnetics as well as electromagnetic scattering. The solution procedure and the associated Matlab codes are developed to simulate the electromagnetic radiation on a biological tissues. The scattered and the total electrical fields are computed over the complex geometry of a brain section with realistic material properties. A perfectly matched layer (PML) is developed to model the far field boundary condition. The IGA platform developed here offers a reliable simulation within an accurate representation of the geometry. The results of this research can be used both in evaluating the potential health and safety risks of electromagnetic radiations and in optimizing the design of radiating devices used in non-invasive diagnostics and therapies

The Principles behind Targeted Therapy for Cancer Treatment

The advent of molecular and genetic advancement in the field of oncology research has led to a shift in the treatment of various forms of cancer from traditional chemotherapeutics to targeted therapy. The principle behind targeted therapy is utilizing therapeutics designed to interfere with specific molecules that have a relatively specific or higher expression profile in cancer cells and are critical for cancer growth and progression. These were designed as mechanistic therapeutics in the form of small molecules and monoclonal antibodies. Currently, they have been modified to incorporate passive or active targeting delivery systems to improve their specific distribution and enhance cytotoxicity towards cancer cells while simultaneously reducing their systemic toxicity profile. Passive targeting employs encapsulated delivery systems to take advantage of the enhanced permeation and retention effect of the tumor microenvironment, while active targeting relies on receptor mediated interactions, such as cell surface ligands conjugated to the therapeutic moiety. A synergistic strategy for cancer therapy is evolving, where precision medicine acts as a diagnostic prerequisite for targeted therapy via prognostic biomarkers and tumor genotyping. Despite the plethora of research undertaken in targeted therapy, limited numbers were approved for clinical use, and significant challenges remain to be addressed

Role of Aquaporins in Breast Cancer Progression and Metastasis

There are various limitations regarding the current pharmacological options for the treatment of breast cancer in terms of efficacy, target selectivity, side effect profile and survival. Endocrine-based therapy for hormone-sensitive cancers such as that of the breast is one of the most effective and well-tolerated therapeutic options but is hampered by either intrinsic or acquired resistance, resulting in a more aggressive form of the disease. It is generally agreed that this process occurs in parallel with cellular transition from epithelial to mesenchymal phenotype (EMT), with consequent enhancement of proliferative capacity, migrative ability and invasive potential. Aquaporins (AQPs) represent a large family of water channel proteins which are widely distributed in various tissues and which play a role in the physiological maintenance of the extracellular environment particularly to regulate electrolyte-water balance. Accumulating evidence shows that expression of several AQPs is modulated in cancer tissues, and this correlates with tumor grade. AQPs 1 and 3–5 are also involved in breast cancer invasion, through modulating the activity of various growth factors, signaling molecules and proteolytic enzymes. We review current data on the involvement of these proteins in processes associated with malignant progression and discuss possible applications of AQP-based therapy as an effective means of inhibiting cancer cells from metastasizing

H2S donor GYY4137 ameliorates paclitaxel-induced neuropathic pain in mice

Paclitaxel-induced neuropathic pain (PINP) is a dose-limiting side effect that largely affects the patient’s quality of life and may limit the use of the drug as a chemotherapeutic agent for treating metastatic breast cancer and other solid tumors. Recently, a putative role for the gaseous mediator hydrogen sulfide (H2S) in nociception modulation has been suggested. The aim of the present study was to investigate the potential efficacy of the slow release H2S donor GYY4137 to alleviate and prevent PINP. Female BALB/c mice that were intraperitoneally (i.p.) injected with paclitaxel (2 mg/kg) for 5 consecutive days developed thermal hyperalgesia, cold and mechanical allodynia and had reduced of H2S, generation in the spinal cord and paw skin. Treatment of mice with established thermal hyperalgesia with GYY4137 or the analgesic positive control drug gabapentin produced antihyperalgesic activities. The antihyperalgesic activity of GYY4137 was antagonized by the ATP sensitive potassium channels (KATP channels) blocker glibenclamide. Co-treatment with GYY4137 and paclitaxel prevented the paclitaxel-induced decrease in H2S, generation as well as the paclitaxel-induced thermal hyperalgesia, cold allodynia and mechanical allodynia. GYY4137 enhanced paclitaxel\u27s anti-proliferative effects against the breast cancer cell line MCF-7. The present results suggest that GYY4137 alleviates paclitaxel-induced thermal hyperalgesia, via KATP channels. GYY4137 prevents PINP possibly by blocking the paclitaxel-induced reduction in the generation of H2S, in the tissues, while enhancing the anti-cancer activity of paclitaxel, and therefore warrants further research as a candidate for prevention of PINP in clinical settings

Enhancing Transparency in the Built Environment: A Dynamic Life Cycle Assessment Approach

The built environment is a major contributor to global greenhouse gas emissions, accounting for 40% of the total. Construction materials, particularly cement, have a significant impact on embodied carbon. However, traditional life cycle assessments (LCAs) use a single score value to quantify embodied carbon, which ignores the dynamic nature of emissions over the lifecycle of a building. As the LCA encompasses an increasing number of lifecycle stages, emissions are released at different points during a given period of analysis, and the resulting global warming impact may not simply be the sum of those emissions. Dynamic LCA (DLCA) has been proposed as a more effective framework for quantifying global warming impact, taking into account the time-dependent emissions inventory. This study explores the application of DLCA in the built environment and investigates its effectiveness compared to traditional LCA. As the use of carbon-storing materials has been identified as a potential strategy for mitigating embodied carbon, this study employs DLCA to examine the necessity of dynamic analysis when considering emissions inventories that have carbon uptake. The study further uses DLCA to evaluate how concrete carbonation can be leveraged to store carbon to reduce its environmental impact. Lastly, a DLCA is applied to a case study of three functionally equivalent structural systems: reinforced concrete, composite steel, and mass timber. The study's findings recommend employing DLCA when a building&rsquo;s emissions inventory becomes are distributed throughout the analysis period considered. It also provides an emphasis of end-of-life concrete treatment over carbon sequestration during the service life phase for effective climate change mitigation in the long run. By conducting DLCA at a systems level, the study highlights that interpreting traditional and dynamic LCA separately can lead to different conclusions. The nuanced interpretation of DLCA can enhance transparency in reporting global warming impact.</p

Optimizing Game Engagement Via Nonparametric Models and Manipulations of Difficulty, Tension, and Perceived Performance

I study the impact of novel game manipulations on user engagement using principled computational methods. Maximizing user engagement is important because it results in more profitable games in the commercial arena and better learning outcomes in the educational arena. It is then perhaps unsurprising that the study of user engagement is well established. Most work uses a classical A/B paradigm, in which a few, often binary (on/off), design decisions are manipulated. Recently, optimization studies have begun to explore a range of discrete or continuous levels. The majority of work in both types of studies is concerned with manipulations such as aesthetics, rewards, and difficulty. While many of these manipulations are found to increase engagement, little work has been done on utilizing theories of engagement from other domains, such as gambling and storytelling, to improve user game engagement. For instance, the tension-and-release manipulation, a common technique in storytelling and music composition for controlling event progression, is usually discussed within the gaming context only qualitatively as a way of controlling difficulty over time. The near-win effect—an increase in motivation due to almost winning a game—comes from gambling psychology. Another understudied manipulation is the perception of difficulty, where the user's perception of the challenge is controlled independently from actual challenge or vice versa. Undoubtedly, game designers are using these manipulations—near-win, tension-release and perception of difficulty—in their games but I am not aware of work that systematically explores how different levels of these manipulations influence user engagement. In this thesis I study these manipulations systematically using Gaussian processes, neural network, and preference learning models. Results from multiple Bayesian optimization experiments show that maximum engagement occurs when the user's perception of difficulty is manipulated moderately, suggesting the critical role of a user's self-perception of competence. A/B and random assignment studies show that engagement in a web-based memory training game can be modulated via tension-and-release difficulty curves. Finally, a massive study with thousands of students shows that the near-win effect significantly improves engagement of lower-performing students