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From Integration to Isolation: Xinjiang Cotton and Commercial Networks (1759-1890)
Get PDFThis dissertation examines the transformation of Xinjiang from the mid-eighteenth to the late nineteenth century within the broader context of global capitalist expansion. Following the Qing conquest in 1759, cotton and cotton textiles produced in Altishahr (southern Xinjiang) and Turpan became highly sought-after commodities in transnational trade, positioning Xinjiang as a central hub within the Eurasian commercial network. However, by the mid-nineteenth century—amid warfare, Russian expansion, and industrialization—Xinjiang gradually became isolated from surrounding markets and, by the early twentieth century, was relegated to a peripheral role as a supplier of raw cotton within the global capitalist system. Adopting a commodity chain approach, this study analyzes the production, transportation, and distribution of cotton and textiles to illuminate the changing institutional, political, and socio-economic landscapes of Qing Xinjiang. Drawing on Manchu, Chinese, and Chaghatay sources, it examines Qing economic governance and frontier policies through the lens of local production, a perspective that remains underexplored in existing scholarship.
While historians have often argued that the rise of maritime trade in the early modern period led to the inevitable decline of overland Eurasian trade networks, this dissertation challenges this narrative. It reveals the persistence—and transformation—of a new kind of Eurasian commercial network during the eighteenth and nineteenth centuries, characterized by merchant capitalism: the dominance of merchants in conducting long-distance trade in bulk commodities without altering the production process. Therefore, Qing China remained deeply integrated into both overland and maritime networks. However, restrictive Qing policies on the mobility of Altishahr merchants ensured that this trade was largely dominated by actors outside of the region, namely Central Asian and Chinese merchants. As a result, Xinjiang functioned primarily as a production site, leaving it especially vulnerable to external market fluctuations. As this interconnected Eurasian commercial system underwent reorganization beginning in the mid-nineteenth century, Xinjiang experienced significant economic dislocation and political instability. In this way, the dissertation contributes to a more nuanced understanding of a connected Eurasian world undergoing profound transformation amid the global expansion of capitalism.East Asian Languages and Civilization
Surface Engineering of T Cells for Improved Adoptive Cell Therapy
No full textT cells, one of the most abundant immune cell types in the human body, play a central role in diverse pathophysiologies and currently dominate the clinical landscape of cell therapies. Their inherent target specificity and ability to induce durable therapeutic responses position them as a powerful modality for precision therapy. Today, T-cell therapies represent a leading frontier in cellular medicine, with extensive ongoing efforts to fully harness their therapeutic potential. Their applications span a broad spectrum of indications, from cancer to autoimmune diseases. In my dissertation, I begin with a detailed analysis of this clinical landscape of adoptive T-cell therapies. The historical milestones that have shaped the evolution of T cells into transformative therapies are outlined, followed by a comprehensive analysis of their clinical translation. This work offers a quantitative and contextualized view of the progress made in T-cell therapy to date. The analysis highlights that the majority of T-cell therapeutic applications seek to leverage their cytotoxic capabilities to eliminate malignant cells, particularly in the context of cancer treatment. Remarkable therapeutic responses observed in hematological malignancies following adoptive cell transfer (ACT) of cytotoxic T lymphocytes (CTLs), specifically CAR T cells, have been pivotal in driving the clinical translation of T-cell therapies. However, despite promising advancements over the past decade, their application to solid tumors has remained relatively limited due to a range of biologically unique challenges. Studies in newly established tumor models have shown that naturally occurring antitumor CTLs gradually lose control over tumor growth due to the development of immune evasion mechanisms within the solid tumor microenvironment (sTME). This sTME, predominantly immunosuppressive in nature, similarly hinders adoptively transferred CTLs. To enhance the efficacy of ACT in solid tumors, there is growing interest in developing strategies that can sustain and reinforce CTL functionality post-ACT.
Under physiological conditions, CTLs maintain their fitness and survival through tonic stimulation, a form of low-level signaling during routine immunosurveillance. In vitro studies have shown that steric exclusion of CD45-phosphatase can effectively initiate this form of tonic stimulation in CTLs. Building on these insights, my dissertation research presents a biomaterial-based strategy for surface engineering of CTLs via phosphatase exclusion to deliver tonic stimulation that locally reinforces their functionality post-ACT. This cell surface engineering platform uses phosphatase-excluding micropatches to drive tonic stimulation and sustain CTL functionality after infusion. First, the design of Polymeric Micropatches for CD45-Phosphatase Exclusion (PMPEs) is described, featuring a core composed of a polylactic-co-glycolide (PLGA) biopolymer. PMPEs bind efficiently to CTLs and induce micron-scale exclusion of CD45-phosphatase at the cell contact interface. This exclusion, validated through probabilistic mathematical modeling and experimental analysis, offers insights into the design features of PMPEs as an effective means for CTL stimulation. A comprehensive set of in vitro assays, including bulk RNA sequencing, calcium flux analysis, cytokine secretion profiling, immunofluorescence, and cytotoxicity assays, alongside in vivo studies, such as biodistribution analysis, immunophenotyping, and pathology assessment, demonstrate that PMPE-modified CTLs exhibit enhanced persistence, robust type 1 immuno-permissive responses, and excellent tolerability. In aggressive B16F10 melanoma and EG7 solid lymphoma mouse models, PMPE-modified CTLs significantly enhance tumor control and extend survival. Notably, the combination of PMPE-CTLs with systemic IL-15 superagonist (IL15sa) therapy in B16F10 melanoma-bearing mice results in 46% survival beyond 35 days, with 15.4% of mice achieving complete tumor remission, compared to none in the IL15sa + CTL group alone. PMPEs represent a simple yet highly promising biomaterial-based approach for sustaining functionality post-transfer and enhancing the therapeutic efficacy of CTLs in solid tumors.
The probabilistic mathematical model from the PMPE study indicates micron-scale close contact formation as the primary driver of tonic stimulation. Supporting this, experimental studies show that PMPEs functionalized with either anti-CD45 or anti-CD44 antibodies induced robust stimulation, whereas soluble antibodies or polymeric nanoparticles failed to do so. These findings highlight the material-independent and purely contact-driven nature of this reinforcement strategy, emphasizing its strong translational potential. To further substantiate this principle, Hydrogel Micropatches for Phosphatase Exclusion (HMPEs) based surface engineering was developed, using a hyaluronic acid (HA) hydrogel core, a material platform markedly distinct from PLGA-based PMPEs. HMPEs exhibited significantly greater hydrophilicity (87° smaller contact angle with water) and ~470-fold lower stiffness compared to PMPEs. Despite these differences, HMPEs engineered to establish firm micron-scale contacts with CTLs successfully promoted CD45 exclusion and reinforced CTL fitness. Altogether, these findings highlight the versatility of the contact-driven reinforcement strategy, laying the foundation for broadly applicable biomaterial interventions. This approach offers future opportunities to integrate drug-loading strategies, tune mechanical properties, and incorporate additional immunomodulatory molecules to further improve adoptive cell therapies across diverse clinical settings.Engineering and Applied Sciences - Engineering Science
U.S. Rural-Urban Breast Cancer Screening Inequities: Leveraging Contextual Heterogeneity to Identify Solutions
Get PDFBreast cancer screening is a critical tool for early detection and prevention, yet rural populations in the U.S. face persistent inequities in access and uptake. Existing research often treats rural communities as a monolith, overlooking contextual differences that may shape health outcomes. This dissertation unpacks rural settings' multidimensional characteristics to better understand and address inequities in breast cancer screening. Drawing on the Community Capitals Framework and guided by Public Health Critical Race Praxis, this mixed-methods dissertation examines how varying rural contexts influence health behaviors and aims to develop tools for identifying place- and equity-centered intervention pathways.
The first paper addresses the limitations of standard rural definitions by developing a novel typology of rurality at the census tract level using latent class analysis (LCA). To do so, we compiled nationally representative data across seven domains of community capital — natural, cultural, human, social, political, financial, and built — for all rural census tracts in the U.S. (n=15,643). The LCA identified four distinct rural typologies: Outlying, Developed, Well-Resourced, and Adaptable Rural. These rural types varied in community capital profiles and social vulnerability, underscoring the need for more nuanced definitions in population health.
Paper two builds from these findings using qualitative methods to explore how contextual heterogeneity shapes screening behaviors in two rural types, Outlying and Well-Resourced, in South-Central Washington State. Community focus groups identified how barriers such as gender norms, seasonal labor, geographic isolation, and uneven resource distribution emerge across community capitals. Though both rural types reported barriers, their underlying mechanisms differed. Four key themes emerged: seasonality and resource prioritization; distance as a resource-dependent barrier; gender roles and healthcare access; and race and place shaping resource distribution. These findings suggest that targeting barriers that cut across mechanisms, such as cultural and social barriers, may lead to larger impacts than uniform interventions.
The third paper introduces a prototype agent-based model (ABM) simulating breast cancer screening behavior across the four rural types identified in Paper 1, using qualitative findings from Paper 2 to inform model rules and contextual parameters. Simulated agents represent screening-eligible women with four characteristics: remoteness, racial identity, poverty status, and insurance status. We found that screening behavior evolves based on these attributes and social network influence. Results show that insurance, poverty, and social connectivity most strongly shape outcomes. Social connectivity improved screening across all groups but had a more pronounced impact among insured and higher-income agents, suggesting connectivity may amplify access where structural barriers are lower. As a theory-building tool, the model clarifies which contextual factors may influence screening and provides a platform to test targeted multilevel interventions before implementation.
Together, these studies challenge the notion of rurality as a singular category and propose an equity-centered framework for understanding how place-based differences shape health. By integrating quantitative, qualitative, and simulation methods, this dissertation advances efforts to design interventions aligned with the unique strengths and needs of diverse rural communities. These findings highlight the importance of context-specific, justice-oriented approaches to achieving health equity in cancer prevention and control.Population Health Science
Mechanisms driving the hypoxic rescue of ferredoxin and lipoate deficiency
No full textThe introduction of environmental oxygen on earth opened new avenues of
biochemistry that enabled rapid evolution of greater complexity across the tree of life.
However, oxygen levels must be maintained carefully to prevent toxic reactions that can
result from toxic radical accumulation and oxidation reactions of critical cofactors.
Oxygen is the most utilized substrate in the human body, and the largest consumer of
oxygen is the electron transport chain in the mitochondria. Thus, mitochondrial function
and oxygen tensions are inextricably linked together, and much of the work done in
studying cellular adaptations to varying oxygen tensions has uncovered critical
mitochondrial adaptations that alter functional capacity to maintain critical energetic
functions under varying oxygen tensions. Recent work from the lab has suggested that
various defects in mitochondrial function may benefit from exposure to low ambient
oxygen. These defects not only included lesions in the electron transport chain, but also
in other mitochondrial pathways such as the synthesis of iron sulfur clusters. Whether
these diverse models of mitochondrial dysfunction are rescued by shared or distinct
mechanisms under hypoxia remains unknown.
I initially began my thesis studies examining the mitochondrial ferredoxin FDX2
and its role in iron sulfur cluster (ISC) synthesis. Ferredoxins are iron sulfur cluster
proteins that serve as single electron donors. FDX2 is known to contribute electrons to
the mitochondrial iron sulfur cluster assembly complex. A screen done in C. elegans in
the lab had identified FDX2 mutations as suppressing phenotypes caused by the loss of
FXN, an allosteric regulator of the ISC machinery. FXN had previously been found to be
dispensable under low oxygen tensions in human cells and C. elegans. We investigated
these FDX2 mutants in human cells and surprisingly found that overexpression of FDX2
resulted in a suppression of ISC synthesis in cells with normal FXN expression in
normoxic, but not hypoxic oxygen tensions. Further work from collaborators confirmed
that FDX2 and FXN required a fixed stoichiometry for optimum ISC machinery activity.
Studies from other labs’ revealed the structure of the ISC machinery and showed that
FDX2 and FXN competed for the same binding site on the structure. These pieces of
evidence helped clarify our findings, suggesting that FDX2 overexpression inhibited
FXN binding and subsequent activity. Our findings may provide clues into the potential
mechanisms of FXN dispensability in low oxygen tensions.
I was next intrigued by the fact that human mitochondria possessed a second
ferredoxin – FDX1 – which was also localized to the mitochondrial matrix and had 50%
identity with FDX2. Both ferredoxins were reported to receive electrons from the same
reductase, FDXR. However, despite the similarities, the two ferredoxins were always
reported to deliver electrons to distinct pathways, with FDX1 previously found to
function in sterol synthesis and FDX2 in iron sulfur cluster synthesis. Additionally, FDX1
scored as dispensable in our labs’ previously published low/high oxygen CRISPR
screen, while FDX2 did not. I was curious about investigating the discrepancies
between these two proteins. Using a combination of CRISPR knockout studies in low
and high oxygen tensions and proteomics, I confirmed that FDX1, but not FDX2, was
dispensable for human cell proliferation under low oxygen tensions. Using TMT Proteomics, I found that FDX1 and FDX2 knockouts had different proteomic profiles.
Finally, using protein modeling by Alpha Fold, I found that FDX1, but not FDX2 was
required for the synthesis of the mitochondrial cofactor lipoate. Intriguingly, lipoate levels
were not restored in these knockouts under low oxygen, and we confirmed that the
lipoate synthesis enzyme LIAS was also dispensable under low O2, leading us to
conclude that the cofactor might in fact be dispensable for human cell proliferation
under low oxygen, identifying yet another model of mitochondrial dysfunction that could
be dispensable under low oxygen tensions.
Finally, I sought to understand the mechanism behind hypoxic rescue of lipoate
deficiency, and how this mechanism was similar or different to that driving rescue of
ETC and OXPHOS disruptions. I found that the inhibition of multiple ETC complexes,
OXPHOS (CV) and loss of LIAS all lead to fitness defects at 21% O2 that are alleviated
at 1% O2 in HepG2 cells. HIF activation was sufficient for rescue of LIAS and Complex II
inhibition, and partially sufficient for rescue of other ETC or OXPHOS defects. Hypoxia
broadly remodeled our HepG2 model cell line, and LIAS KO cells were rescued through
a combination of increased glycolytic flux, reductive carboxylation through pyruvate
carboxylase (PC), and activation of carbonic anhydrase 9 (CA9) under hypoxia.
However, neither PC nor CA9 are needed for rescue of ETC or OXPHOS under
hypoxia. Thus, we were able to identify a specific mechanism for hypoxic rescue of a
defect in mitochondrial metabolism and conclude that the broad remodeling under
hypoxia allowed for a myriad of distinct mechanisms to concurrently exist, allowing
rescue of diverse mitochondrial insults.Biological and Biomedical Science
When Values Guide the Way: The Relationship Between Value-Congruent Behavior and Resilience Through the Pathway of Perceived Stress.
Get PDFValues are fundamental beliefs that guide individuals’ decisions and behaviors.
When people act in alignment with their personal values, they tend to experience less
inner conflict and better emotional regulation. In contrast, acting against one’s values can
increase internal conflicts, discomfort and stress. Stress arises when perceived demands
are overwhelming or exceed a person’s abilities. In such cases, stress can negatively
affect mental health and resilience. Helping individuals manage their perception of stress
by encouraging behavior that aligns with their values may support their resilience—the
capacity to adapt and recover from difficulties with mental health as an outcome.
This study examined how value-congruent behavior relates to perceived stress and
resilience. Using an online survey, participants reported their alignment with values,
perceived stress, and resilience. The results suggested that behaving in accordance with
one’s values was linked to lower perceived stress and higher resilience. The relationship
between value-congruent behavior and resilience was fully mediated by perceived stress.
These findings highlight the role of value-congruent behavior in managing stress
and enhancing resilience. Encouraging individuals to act according to their values may be
an effective strategy in psychological interventions aimed at improving stress
management and increasing long-term well-being. Future research should explore these
relationships and test causality with experimental intervention designs, over time, and in
different populations.Extension Studie
Using Capillary Forces to Manipulate Microscopic Objects
Get PDFCapillary interactions act between objects that deform a fluid-fluid interface. In recent decades, such interactions have been used to self-assemble objects at interfaces into complex structures. These assembly processes typically do not allow for direct control over individual particle trajectories. In this work, I describe machines and tweezers that use capillary interactions to manipulate small objects in programmable trajectories along fluid-fluid interfaces. Capillary machines are 3D-printed devices that can be used to manipulate millimeter-scale floating objects. I demonstrate that these machines can be used to braid, twist, and weave fibers that are too small to be manipulated with conventional machines. Capillary tweezers are a new kind of tweezer that can be used to manipulate single colloidal microspheres. I demonstrate that these tweezers can trap and translate a single particle and can be used to measure forces applied to the trapped particle.Engineering and Applied Sciences - Applied Physic
Ensemble Methods for Latent Structure Detection from Heterogeneous Genomic and Phenotypic Data
Get PDFDisentangling the hidden patterns within genomic and phenotypic data can improve our understanding of complex conditions. Recent methodological developments in statistics and machine learning have improved our ability to detect latent patterns in a variety of application areas; however, these methods are often unsuitable for some of the data types common to health and biomedical data. Likewise, many latent structure methods require prespecification of the dimensions of the latent space, which is typically unknown. In this work, we introduce three ensemble statistical and machine learning methods designed to fill in these gaps.
In Chapter 1, we introduce LACE-UP (LAtent Class analysis Ensembled with Umap and Pca), an ensemble machine learning method that outperforms gold-standard and oracle methods for clustering multidimensional binary data. When applied to dietary behavior data from the UK Biobank, LACE-UP uncovers interpretable dietary subtypes that are associated with lipid levels and cardiovascular risk. In Chapter 2, we introduce SEEK-VEC (Spectral Ensembling of topic models with Eigenscore for K-agnostic Vocabulary Embedding and Classification), a spectral ensemble topic modeling method for count data that yields prioritization scores and grouping scores that enable variable classification, pattern detection, and model diagnostics. We show through simulations that SEEK-VEC outperforms standard methods, particularly in weaker signal strength settings. We apply SEEK-VEC to single-cell gene expression data, food preference questionnaire data, and self-reported psychopathology symptom data, and show that the method uncovers meaningful insights across a broad range of contexts. In Chapter 3, we introduce SEEK-VFI (Spectral Ensembling of topic models with Eigenscore for K-agnostic Variable Feature Identification), an extension of SEEK-VEC that ranks genes with respect to their relevance to cell trajectory structure. We show that SEEK-VFI outperforms leading methods for differentiating between trajectory-relevant and uninformative genes, and we apply SEEK-VFI to several single-cell RNA expression datasets and demonstrate its ability to recover the true trajectory structure within the data.
This suite of methods, designed for non-continuous data, provide a lens into the latent structure underlying phenotypic and genomic data. These methods do not require the prespecification of the dimensions of the latent space and are robust to noise. Taken together, the promise of these methods and the development of similar methods in the future is a refined understanding of complex phenotypes and their underlying mechanisms, which in turn will improve diagnoses, prognoses, and care.Biostatistic
Theoretical and Machine Learning Modeling and DFT Simulations in Energy-Related Materials and Devices
No full textModern energy-related materials often exhibit complex, multiscale interactions involving strong coupling between mechanical, electronic, and diffusion degrees of freedom. This dissertation addresses key challenges in understanding and predicting the behavior of such systems through an integrated approach that combines analytical modeling, first-principles simulations, experimental interpretation, and machine learning techniques.
We begin by investigating solid-state batteries (SSBs) interfaces, where interfacial degradation and dendrite formation limit performance and reliability. A continuum model is developed to describe how strain-induced suppression of diffusivity leads to a self-limiting reaction mechanism. The model unifies previously fragmented insights into a general theoretical framework that explains self-limiting reaction behavior in a wide range of materials and interface types by quantitative modeling works. Large-scale simulations further connect local self-limiting kinetics with experimentally observed degradation morphology patterns. This work broadens the design space for next-generation batteries from the perspective of dynamic stability design, emphasizing interface engineering and transport dynamics coupled with reaction induced local strain-stress field as critical levers for achieving long-term stability and reliability.
In the context of high-temperature superconductors, the group’s material-dependent DFT computations reveal strong lattice–charge–magnetic coupling in cuprate superconductors. By analyzing anharmonic phonon modes and charge redistribution patterns, dynamic charge fluxes patterns were extracted that unveils their correlation with superconducting T_c. Building on this insight, my work focuses on constructing a quantum theoretical framework where flux fluctuations—characterized by wavevector oscillation and coherence width—mediate an attractive interaction kernel. Theoretical analysis within the random phase approximation (RPA) leads to a derived scaling law for T_c, showing strong dependence on material-specific anharmonicity and coupling strength. These results suggest that flux-mediated interactions, emerging from tightly coupled lattice–charge–spin dynamics, may provide a pathway toward understanding and enhancing unconventional superconductivity.
Next, we study Na-layered oxides and unveil critical structural details in NaxCrO2 electrochemical evolution. Combining experimental XRD with DFT data, we develop a structure optimization framework capable of identifying complex Na-vacancy ordering patterns and explain the existence of Na density wave ordering patterns. The result reveals novel nano-stripe-like vacancy domains under low Na composition and resolves puzzles about charge–discharge asymmetry and metastability, offering new principles for cathode design with improved reversibility and structural stability.
Finally, in the field of battery data science, we present a 2D image-based machine learning framework for battery performance prediction. By encoding cycling curves as binary images and training on various models including deep residual networks end-to-end, we demonstrate enhanced predictive accuracy, robustness and interpretability of 2D representation compared to traditional 1D approaches. The work opens new directions for leveraging advanced vision models to electrochemical degradation and lifetime forecasting for batteries.
Together, these studies highlight the power of multi-method modeling in revealing hidden mechanisms, guiding material selection, and proposing novel design strategies for energy related materials.Engineering and Applied Sciences - Applied Physic
Essays in the Economics of Education
Get PDFThis dissertation studies several education policies that relate to labor economics, public economics, and the economics of education using various econometric techniques. This dissertation studies the effects of universal FAFSA policies on student outcomes, inequalities in federal higher education funding, and two of the largest changes to Pell Grant eligibility over the past two decades. This dissertation uses privileged administrative data from the United States Department of Education’s Office of Federal Student Aid, as well as publicly available data from the Integrated Postsecondary Education Data System (IPEDS), Integrated Public Use Microdata Series (IPUMS), and National Center for Education Statistics (NCES).
The first chapter in the dissertation focuses on universal FAFSA policies that require students to apply to the FAFSA as a high school graduation requirement. These policies intended to increase application rates to the Free Application for Federal Student Aid (FAFSA). This paper explores this policy in seven different states (Louisiana, Illinois, Alabama, Colorado, Texas, Maryland, and California) and uses synthetic difference-in-differences and synthetic controls to study the effects of this policy on various student outcomes. The paper presents both staggered results that consider the overall effect of these policies as well as evaluations of each state individually. The staggered results demonstrate that universal FAFSA policies have a 7.7 percentage point increase on FAFSA application rates in “strict” states that establish high school graduation requirements that require students to submit a FAFSA application. Reduced form estimates demonstrate that states that adopt a universal FAFSA policy experience an increase in college enrollment rates of about three percentage points by recent high school graduates.
The second chapter focuses on the federal funding of higher education to universities and institutions. This paper presents four descriptive facts about revenue and expenses in higher education. Two important takeaways from this paper are that Ivy+ universities receive roughly 15\% of the overall share of federal higher education funding (excluding Pell Grants). Twelve colleges which make up less than 1.5\% of the overall share of full-time enrolled students receive almost ten times that amount in federal funding. Second, highly selective public universities are greater net investors in research expenditures than Ivy+ universities.
The third chapter studies two changes to Pell Grant eligibility that occurred in 2012 as part of the Consolidated Appropriations Act, 2012. These two changes represent some of the largest changes to Pell Grant eligibility over the past 20 years. The paper uses various donut regression discontinuity designs to evaluate how small changes in the amounts of Pell Grants distributed to students at two different margins affected their behavior. The paper finds that students on the margin of losing all their Pell Grants, despite the absolute amount being quite low (\$305), change their enrollment intensity. Students that lost all their Pell Grant eligibility experienced a 9.3 percentage point decrease in full-time enrollment rates. Additionally, students that lost their Pell Grant eligibility were on average more likely to borrow loans from the federal government. On average students borrowed more than five times (\$1,387) the amount they lost in Pell Grants (\$305). The increase in borrowing behavior was driven by students earlier in their college experience, while students in their later years withdrew at statistically significant higher rates. Students on the margin of losing a small amount relative to their total Pell Grant award did not change their behavior in terms of their enrollment intensity or borrowing behavior.Educatio
The Kremlin’s Conundrum: Telegram as Russia’s Information Battlefield
Get PDFThe project of this thesis is to investigate the dynamics of communication within Russian on a popular online social media platform, in the context of the Ukraine- Russia war. The investigation is structured around three directions: (1) influence, (2) communities, and (3) assessing the causal impact of a government intervention in the informational space.
The individuals that make up our dataset are generally well-established actors in the Russian informational space, with their identity ranging from public figures, to members of the government, to anonymous individuals who have amassed a significant online following.Applied Mathematic