Selective Preparation of Semiconducting Single-Walled Carbon Nanotubes: From Fundamentals to Applications

Aligned single-walled carbon nanotubes (SWNTs) synthesized by the chemical vapor deposition (CVD) method have exceptional potential for next-generation nanoelectronics. However, there are considerable challenges in the preparation of semiconducting (s-) SWNTs with controlled properties (e.g., density, selectivity, and diameter) for their application in solving real-world problems. This dissertation describes research that aims to overcome the limitations by novel synthesis strategies and post-growth treatment. The application of as-prepared SWNTs as functional devices is also demonstrated. The dissertation includes the following parts: 1) decoupling the conflict between density and selectivity of s-SWNTs in CVD growth; 2) investigating the importance of diameter control for the selective synthesis of s-SWNTs; 3) synthesizing highly conductive SWNT thin film by thiophene-assisted CVD method; 4) eliminating metallic pathways in SWNT crossbars by gate-free electrical breakdown method; 5) enhancing the density of SWNT arrays by strain-release method; 6) studying the sensing mechanism of SWNT crossbar chemical sensors.

Dasatinib (BMS-35482) potentiates the activity of gemcitabine and docetaxel in uterine leiomyosarcoma cell lines.

BACKGROUND: To explore the activity of dasatinib alone and in combination with gemcitabine and docetaxel in uterine leiomyosarcoma (uLMS) cell lines, and determine if dasatinib inhibits the SRC pathway. METHODS: SK-UT-1 and SK-UT-1B uLMS cells were treated with gemcitabine, docetaxel and dasatinib individually and in combination. SRC and paxcillin protein expression were determined pre- and post-dasatinib treatment using Meso Scale Discovery (MSD) multi-array immunogenicity assay. Dose-response curves were constructed and the coefficient of drug interaction (CDI) and combination index (CI) for drug interaction calculated. RESULTS: Activated phosphorylated levels of SRC and paxillin were decreased after treatment with dasatinib in both cell lines (p < 0.001). The addition of a minimally active concentration of dasatinib (IC25) decreased the IC50 of each cytotoxic agent by 2-4 fold. The combination of gemcitabine-docetaxel yielded a synergistic effect in SK-UT-1 (CI = 0.59) and an antagonistic effect in SK-UT-1B (CI = 1.36). Dasatinib combined with gemcitabine or docetaxel revealed a synergistic anti-tumor effect (CDI < 1) in both cell lines. The triple drug combination and sequencing revealed conflicting results with a synergistic effect in SK-UT-1B and antagonistic in SK-UT-1. CONCLUSION: Dasatinib inhibits the SRC pathway and yields a synergistic effect with the two-drug combination with either gemcitabine or docetaxel. The value of adding dasatinib to gemcitabine and docetaxel in a triple drug combination is uncertain, but may be beneficial in select uLMS cell lines. Based on our pre-clinical data and known activity of gemcitabine and docetaxel, further evaluation of dasatinib in combination with these agents for the treatment of uLMS is warranted.

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Unnatural History: Ecological Temporality in Post-1945 American Literature

While environmental literary criticism has traditionally focused its attention on the textual representation of specific places, recent ecocritical scholarship has expanded this focus to consider the treatment of time in environmental literature and culture. As environmental scholars, activists, scientists, and artists have noted, one of the major difficulties in grasping the reality and implications of climate change is a limited temporal imagination. In other words, the ability to comprehend and integrate different shapes, scales, and speeds of history is a precondition for ecologically sustainable and socially equitable responses to climate change.

My project examines the role that literary works might play in helping to create such an expanded sense of history. As I show how American writers after 1945 have treated the representation of time and history in relation to environmental questions, I distinguish between two textual subfields of environmental temporality. The first, which I argue is characteristic of mainstream environmentalism, is disjunctive, with abrupt environmental changes separating the past and the present. This subfield contains many canonical works of postwar American environmental writing, including Aldo Leopold’s A Sand County Almanac, Edward Abbey’s Desert Solitaire, Annie Dillard’s Pilgrim at Tinker Creek, and Kim Stanley Robinson’s Science in the Capital trilogy. From treatises on the ancient ecological histories of particular sites to meditations on the speed of climate change, these works evince a preoccupation with environmental time that has not been acknowledged within the spatially oriented field of environmental criticism. However, by positing radical breaks between environmental pasts and environmental futures, they ultimately enervate the political charge of history and elide the human dimensions of environmental change, in terms both of environmental injustice and of possible social responses.

By contrast, the second subfield, which I argue is characteristic of environmental justice, is continuous, showing how historical patterns persist even across social and ecological transformations. I trace this version of environmental thought through a multicultural corpus of novels consisting of Ralph Ellison’s Invisible Man, Ishmael Reed’s Mumbo Jumbo, Helena María Viramontes’ Under the Feet of Jesus, Linda Hogan’s Solar Storms, and Octavia Butler’s Parable of the Sower and Parable of the Talents. Some of these novels do not document specific instances of environmental degradation or environmental injustice and, as a result, have not been critically interpreted as relevant for environmental analysis; others are more explicit in their discussion of environmental issues and are recognized as part of the canon of American environmental literature. However, I demonstrate that, across all of these texts, counterhegemonic understandings of history inform resistance to environmental degradation and exploitation. These texts show that environmental problems cannot be fully understood, nor environmental futures addressed, without recognizing the way that social histories of inequality and environmental histories of extraction continue to structure politics and ecology in the present.

Ultimately, then, the project offers three conclusions. First, it suggests that the second version of environmental temporality holds more value than the first for environmental cultural studies, in that it more compellingly and accurately represents the social implications of environmental issues. Second, it shows that “environmental literature” is most usefully understood not as the literature that explicitly treats environmental issues, but rather as the literature that helps to produce the sense of time that contemporary environmental crises require. Third, it shows how literary works can not only illuminate the relationship between American ideas about nature and social justice, but also operate as a specifically literary form of eco-political activism.