The band structure of a solid crystal is not only dependent on the potential of periodic atoms but also the electron-ion and electron-electron interactions. When dimensionality reduces to two, the latter part becomes prominent owning to greatly enhanced Coulomb interaction between quasiparticles. Meanwhile, the atomic thickness of two-dimensional materials allows full access to the interior of materials through electrostatic doping. Thus, electrostatic doping becomes one main approach to exploit the fundamental physics and practical application in the 2D limit. Among the family of two-dimensional systems, transition metal dichalcogenides (TMDs) are semiconductors with bandgaps, covering a broad spectrum. In addition, new degrees of freedom, such as topological edges and valley index, are found in TMDs and open the door to a new generation of energy-efficient optoelectronic devices carrying more information.

This dissertation first presents optimal thermoelectric effect in TMDs, simultaneously optimizing Seebeck coefficient and electrical conductivity. Even though both of them are determined by the electronic structure of the material, large Seebeck coefficient requires a large asymmetry near Fermi level, opposite to that for electrical conductivity. We employed electrostatic doping to lift up Fermi level and balance this tradeoff. And a recorded-high power factor up to 8.5 mW m−1 K−2 at room temperature was reported here due to its large effective mass and unique density of state under strong quantum confinement. Additionally, Seebeck coefficient at various temperatures reveals the phonon-limited scattering mechanism at two-dimensional materials. If limiting the carrier density to a low level, the electrical conductivity gives a carrier hopping length around 3nm, which indicates the density of active defects inside. The demonstrated high, electronically modulated power factor in 2D TMDCs holds promise for efficient thermoelectric energy conversion. And the discoveries of carriers’ scattering and transport mechanisms are significant to understand quasiparticles’ interaction at low dimensionality.

When the density of quasiparticles goes beyond critical density, quasiparticles not only impact electronic structure but also have the possibility to reconstruct the configuration of lattice. For the first time, we demonstrated monolayer molybdenum ditelluride (MoTe2) reforms form hexagonal phase to monoclinic one through electrostatic doping. A hysteretic behavior has been observed, which is a feature of structural changes as well as the existence of kinetic barrier. Microscopically, the crystal orientation of the electrostatic induced monoclinic phase is found to be consistent with the original crystal orientation of hexagonal phase. It leads to the reversibility of the structural phase transition in terms of both phases and orientation. This discovery opens new possibilities for developing phase-change memory based on atomically thin membranes. The carrier density (metallicity) is also shown to greatly affect the photoluminescence efficiency by controlling the pathway of exciton recombination and hence improves the performance of optoelectronics.

Skeletal muscle undergoes progressive mass loss, termed sarcopenia, and increased accumulation of fibrotic tissue during aging, which reduces the life quality of the elderly and causes significant economic burden on healthcare services of society. The importance of the immune system, especially myeloid cells, in modulating muscle growth and regeneration following injury suggests that myeloid cells may also have significant influences on sarcopenia and muscle fibrosis during aging. In this investigation, we studied the regulatory interactions between myeloid cells and skeletal muscle during aging. We found that muscle aging is associated with increased number of anti-inflammatory M2a macrophages that can increase muscle fibrosis. Expression of a muscle-specific transgene of nNOS prevented the age-related increases in M2a macrophage and reduced fibrosis in aging muscle. We then tested whether aging of myeloid cells contributes to the age-related increases in M2a macrophages and the associated increases in fibrosis. Transplantation of young bone marrow cells into old mice resulted in fewer M2a macrophages and less accumulation of collagen compared to age-matched, non-transplant mice. We also found that transplantation of young bone marrow cells into old mice prevented sarcopenia. On the other hand, muscles of young mice receiving old bone marrow cells showed decreased numbers of muscle stem cells, called satellite cells, and increased numbers of fibrogenic-converted satellite cells. In vitro, media conditioned by young, but not old, bone marrow-derived macrophages increased muscle cell proliferation. These data suggest that aging of myeloid cells promotes the shift of satellites cell from a myogenic lineage to a more fibrogenic lineage during aging, and contributes to sarcopenia and muscle fibrosis. However, both the nNOS transgene and the heterochronic bone marrow transplantation can also affect muscle aging through unknown mechanisms other than changes in myeloid cells. To specifically manipulate the myeloid cell population, we designed a mouse line with a myeloid-cell-specific mutation of transcription factor Sfpi1 which specifically reduced the number of M2 macrophages in muscle. Myeloid-cell-specific mutation of Sfpi1 prevented sarcopenia and age-related muscle fibrosis, strongly suggesting that muscle aging is at least partly attributable to myeloid cells. Previous studies in our lab and by other groups suggested that tumor necrotic factor-alpha (TNFα) is a macrophage-derived factor that may contribute significantly to muscle aging. We found that systemic TNFα knockout increased satellite cell fusion into muscle fibers in old muscle and prevented sarcopenia. Furthermore, we observed that transplantation of wild-type bone marrow cells into TNFα knockout mice induced sarcopenia and reduced muscle cell fusion, indicating that TNFα secreted by myeloid cells contributes significantly to the reduction of satellite cell myogenic capacity during aging and causes sarcopenia. Overall, our findings provide insight into mechanisms of muscle aging and the regulatory interactions between myeloid cells and skeletal muscle during aging.

Capacitively driven radio frequency (rf) discharges are commonly used for plasma-assisted material processing. Because of the significant difference in the mobility of electrons and ions, a thin layer of sheath is always established at the boundary, which separates the discharge into two regions: quasi-neutral bulk plasma and positively charged sheath. Within the sheath, ions are accelerated by electric fields and therefore, bombard the electrode with significant energies. The ion energy distribution (IED) on the substrate is essentially important in the optimization of discharge operations. For plasmas sources typically operated at higher densities and lower pressures, the ion motion in the rf sheath is mainly collisionless since the ion mean free path is much larger than the sheath width. At high operating pressures and large sheath voltage drops, the sheaths are typically collisional.

A fast and simple model consisting of a series of computational steps is of great value to predict the plasma parameters and IEDs, given the control parameters of the discharge. Respective models for IEDs in collisionless and collisional capacitive rf sheaths are developed in this dissertation, based on the sheath models developed in late 1980s. Both models do not rely on any intermediate parameters from simulation or experimental results and only take a few seconds (collisionless) or minutes (collisional) to get the final IEDs. Ion-neutral charge exchange reactions are considered for collisional rf sheaths. Energy dependent ion mean free path is taken into account. Particle-in-cell (PIC) simulations are used to verify the previous sheath models and the IED models.

The PIC code OOPD1, being developed with a powerful capability as the development of the collisional IED model goes on, is introduced in this dissertation. Comparisons with XPDP1 are presented, which confirms OOPD1 as a trustable, friendly, and extensible simulation tool to observe the rf discharges.

My dissertation explores fictional medical narratives in late imperial China by focusing on representations of sickness, with particular attention to the patient. I argue that these fictional medical narratives produced a unique representation of sickness in terms of the patient’s subjective experience of an identity crisis. Through various patients’ individual experiences in fictional medical narratives, it can be seen that “sickness” indicates not only an unhealthy state, but also a process that uncovers disorders of family, ethics, and power structures, and can lead to the remaking of social identities and interpersonal relationships. As the rise of vernacular fiction as a narrative genre during the late Ming was closely related to the contemporary obsession with individual desire, the study of fictional medical narrative will help us understand and contextualize the new emphasis on the value of the individual. This dissertation takes fictional representations of sickness as a lens through which the tension between patients’ subjectivity and socially normative models is made manifest. I define the medical narrative in Ming-Qing vernacular fiction as a story that focuses on a character’s experience of illness and on the way illness influences the character’s social life and interpersonal relationships. In other words, it is the existence and the experience of the patient that constitutes the central feature of fictional medical narratives. Around the existence and the experience of the patient, the other characters have various responses and perform various roles, thereby contributing to the construction of the patient as a distinct type of character. We can conclude that these vernacular stories reflect the humanization of medicine, transforming the typology of patients in non-fictional medical narrative into various descriptions of patients as “whole persons in contexts and relations.” Fictional medical narratives reflect a more complex understanding of the cause, progress, and result of the sickness, revealing how patients’ experiences of suffering go beyond physical symptoms, diagnoses, and treatment. Therefore, fictional medical narratives formulate and convey contemporary understandings of the social meanings of illness in a way not found in other kinds of texts.

Networks pervade many disciplines of science as a way of analyzing complex systems with interacting components. The problem of network modeling is often two-fold. First, the relationships between pairs of nodes, if not directly observed, have to be estimated from data. Based on the estimated (or given) network topology, various statistical and computational tools can then be applied to extract interesting patterns such as the presence of communities. In this thesis we explore studies related to both parts of the problem.

We first discuss two studies in the context of gene regulatory networks, where the goal is to infer gene interactions using expression data. With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to infer gene interactions has been a major challenge in systems biology. The two studies differ in their considerations of how genes behave across the given samples. The first method applies to the case of large heterogenous samples, where the patterns of gene association may change or only exist in a subset of all the samples. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable if not better general performance.

In comparison, the second study goes beyond pairwise gene relationships to higher level group interactions, but requiring similar gene behaviors across all the samples. We introduce a new method for estimating group interactions using sparse canonical correlation analysis (SCCA) coupled with repeated random partition and subsampling of the gene expression dataset. By considering different subsets of genes and ways of grouping them, our interaction measure can be viewed as an aggregated estimate of partial correlations of different orders. Our approach is unique in evaluating conditional dependencies when the correct dependent sets are unknown or only partially known. As a result, a gene network can be constructed using the interaction measures as edge weights and gene functional groups can be inferred as tightly connected communities from the network. Comparisons with several popular approaches using simulated and real data show our procedure improves both the statistical significance and biological interpretability of the results. In addition to achieving considerably lower false positive rates, our procedure shows better performance in detecting important biological pathways.

Moving onto general networks, we then discuss model selection for the stochastic block model (SBM), which is a popular tool for community detection. We consider an approach based on the log likelihood ratio statistic and analyze its asymptotic properties under model misspecification. We show the limiting distribution of the statistic in the case of underfitting is normal and obtain its convergence rate in the case of overfitting. These conclusions remain valid in the regime where the average degree grows at a polylog rate. The results enable us to derive the correct order of the penalty term for model complexity and arrive at a likelihood-based model selection criterion that is asymptotically consistent. In practice, the likelihood function can be estimated by more computationally efficient variational methods, allowing the criterion to be applied to moderately large networks.