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Description我国上市公司股东采取股权质押贷款方式融资非常普遍，股权质押贷款总余额已经高达5.70万亿元，这些贷款背后隐藏着巨大的风险。上市公司大股东采用股权质押贷款的动机可能是对未来乐观行业预期的资金需求，或者是由于股权转让的限制而采取的变相套现转移风险。后者的动机里本身就包含着上市公司大股东对自身股价已经估值过高的判断。本文通过分析上市公司股权质押贷款风险 (即，上市公司当年股价估值程度与上市公司下一年股价崩盘风险之间的关系)会因为股权质押动机的不同而存在差异。只有在套现动机下，当年股价估值程度才会导致下一年显著的股价崩盘风险，而在融资动机下当年股价估值程度与下一年股价崩盘风险之间的关系则呈现负相关。鉴于目前大多数上市公司并不会披露大股东质押所得资金的具体去向，本文通过融资约束程度这个维度对上市公司大股东股权质押的动机进行识别。当上市公司所受融资约束较低时，当年股价高估程度程度越大，下一年股价崩盘风险越大（正相关），上市公司进行股权质押的动机更倾向于高位套现。在融资约束程度高的情况下，上市公司股权质押更倾向于融资，当年股价估值程度越大，下一年上市公司股价崩盘风险越小（负相关）。 在大股东控制权高的情况下，对于所受融资约束程度低的上市公司，独立董事不论是比例高或者低，独立董事制度对大股东的股票质押的行为 (套现动机)无影响。 对于所受融资约束程度高的上市公司，独立董事在占比高时，对通过股票质押来融资的行为有强化作用，可以表现出其治理影响力，在独董占比低的情况下则无法产生作用。
ContributorsChen, Wei (Author) / Pei, Ker-Wei (Thesis advisor) / Jiang, Zhan (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2020
Description多元化公司整体上市和分拆上市的理论与实践在国际市场已经较为成熟，而对于中国A股来说，分拆上市一直受制于政策；2019年以来，随着科创板的开启及A股将迎来的改革，多元化公司分拆上市也将迎来重要机遇。
多年来，学术界对于多元化公司在资本市场方面的研究主要聚焦于估值比较、相关影响因素以及整体上市和者分拆上市的法律、财务、风险等方面。而本文在已有理论基础上，从股权集中度、新兴行业、跨市场上市（A+H）三个方面研究中国多元化公司的整体或者分拆上市的行为选择，研究发现：大股东持股比例更高的公司更倾向于整体上市，新兴行业或者创新性的子公司更可能被分拆上市，跨多个股票市场上市的公司更倾向分拆上市子公司，这与委托代理理论和信息不对称理论的预测一致。
为了让研究更加全面并具实用性，本文加入了对中国平安集团和温氏集团的案例分析，还针对中介投行和研究人员的特定人群进行分析式调研，结果均较好支持了研究结论，使得本文的研究兼具理论创新和实际指导价值。
关键词: 中国多元化公司，整体上市，分拆上市，股权集中度，新兴行业，跨市场上市
多年来，学术界对于多元化公司在资本市场方面的研究主要聚焦于估值比较、相关影响因素以及整体上市和者分拆上市的法律、财务、风险等方面。而本文在已有理论基础上，从股权集中度、新兴行业、跨市场上市（A+H）三个方面研究中国多元化公司的整体或者分拆上市的行为选择，研究发现：大股东持股比例更高的公司更倾向于整体上市，新兴行业或者创新性的子公司更可能被分拆上市，跨多个股票市场上市的公司更倾向分拆上市子公司，这与委托代理理论和信息不对称理论的预测一致。
为了让研究更加全面并具实用性，本文加入了对中国平安集团和温氏集团的案例分析，还针对中介投行和研究人员的特定人群进行分析式调研，结果均较好支持了研究结论，使得本文的研究兼具理论创新和实际指导价值。
关键词: 中国多元化公司，整体上市，分拆上市，股权集中度，新兴行业，跨市场上市
ContributorsSong, Guoping (Author) / Gu, Bin (Thesis advisor) / Li, Feng (Thesis advisor) / Wu, Fei (Committee member) / Arizona State University (Publisher)
Created2020
Description
Phononic crystals are artificially engineered materials that can forbid phonon propagation in a specific frequency range that is referred to as a “phononic band gap.” Phononic crystals that have band gaps in the GHz to THz range can potentially enable sophisticated control over thermal transport with “phononic devices”. Calculations of the phononic band diagram are the standard method of determining if a given phononic crystal structure has a band gap. However, calculating the phononic band diagram is a computationally expensive and time-consuming process that can require sophisticated modeling and coding. In addition to this computational burden, the inverse process of designing a phononic crystal with a specific band gap center frequency and width is a challenging problem that requires extensive trial-and-error work.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
In this dissertation, I first present colloidal nanocrystal superlattices as a new class of three-dimensional phononic crystals with periodicity in the sub-20 nm size regime using the plane wave expansion method. These calculations show that colloidal nanocrystal superlattices possess phononic band gaps with center frequencies in the 102 GHz range and widths in the 101 GHz range. Varying the colloidal nanocrystal size and composition provides additional opportunities to fine-tune the phononic band gap. This suggests that colloidal nanocrystal superlattices are a promising platform for the creation of high frequency phononic crystals.
For the next topic, I explore opportunities to use supervised machine learning for expedited discovery of phononic band gap presence, center frequency and width for over 14,000 two-dimensional phononic crystal structures. The best trained model predicts band gap formation, center frequencies and band gap widths, with 94% accuracy and coefficients of determination (R2) values of 0.66 and 0.83, respectively.
Lastly, I expand the above machine learning approach to use machine learning to design a phononic crystal for a given set of phononic band gap properties. The best model could predict elastic modulus of host and inclusion, density of host and inclusion, and diameter-to-lattice constant ratio for target center and width frequencies with coefficients of determinations of 0.94, 0.98, 0.94, 0.71, and 0.94 respectively. The high values coefficients of determination represents great opportunity for phononic crystal design.
ContributorsSadat, Seid Mohamadali (Author) / Wang, Robert Y (Thesis advisor) / Huang, Huei-Ping (Committee member) / Ankit, Kumar (Committee member) / Wang, Liping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
Recently, a well-designed and well-trained neural network can yield state-of-the-art results across many domains, including data mining, computer vision, and medical image analysis. But progress has been limited for tasks where labels are difficult or impossible to obtain. This reliance on exhaustive labeling is a critical limitation in the rapid deployment of neural networks. Besides, the current research scales poorly to a large number of unseen concepts and is passively spoon-fed with data and supervision.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
ContributorsZhang, Jie (Author) / Wang, Yalin (Thesis advisor) / Liu, Huan (Committee member) / Stonnington, Cynthia (Committee member) / Liang, Jianming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Soft materials are matters that can easily deform from their original shapes and structures under thermal or mechanical stresses, and they range across various groups of materials including liquids, foams, gels, colloids, polymers, and biological substances. Although soft materials already have numerous applications with each of their unique characteristics, integrating materials to achieve complementary functionalities is still a growing need for designing advanced applications of complex requirements. This dissertation explores a unique approach of utilizing intermolecular interactions to accomplish not only the multifunctionality from combined materials but also their tailored properties designed for specific tasks. In this work, multifunctional soft materials are explored in two particular directions, ionic liquids (ILs)-based mixtures and interpenetrating polymer network (IPN).
First, ILs-based mixtures were studied to develop liquid electrolytes for molecular electronic transducers (MET) in planetary exploration. For space missions, it is challenging to operate any liquid electrolytes in an extremely low-temperature environment. By tuning intermolecular interactions, the results demonstrated a facile method that has successfully overcome the thermal and transport barriers of ILs-based mixtures at extremely low temperatures. Incorporation of both aqueous and organic solvents in ILs-based electrolyte systems with varying types of intermolecular interactions are investigated, respectively, to yield optimized material properties supporting not only MET sensors but also other electrochemical devices with iodide/triiodide redox couple targeting low temperatures.
Second, an environmentally responsive hydrogel was synthesized via interpenetrating two crosslinked polymer networks. The intermolecular interactions facilitated by such an IPN structure enables not only an upper critical solution temperature (UCST) transition but also a mechanical enhancement of the hydrogel. The incorporation of functional units validates a positive swelling response to visible light and also further improves the mechanical properties. This studied IPN system can serve as a promising route in developing “smart” hydrogels utilizing visible light as a simple, inexpensive, and remotely controllable stimulus.
Over two directions across from ILs to polymeric networks, this work demonstrates an effective strategy of utilizing intermolecular interactions to not only develop multifunctional soft materials for advanced applications but also discover new properties beyond their original boundaries.
First, ILs-based mixtures were studied to develop liquid electrolytes for molecular electronic transducers (MET) in planetary exploration. For space missions, it is challenging to operate any liquid electrolytes in an extremely low-temperature environment. By tuning intermolecular interactions, the results demonstrated a facile method that has successfully overcome the thermal and transport barriers of ILs-based mixtures at extremely low temperatures. Incorporation of both aqueous and organic solvents in ILs-based electrolyte systems with varying types of intermolecular interactions are investigated, respectively, to yield optimized material properties supporting not only MET sensors but also other electrochemical devices with iodide/triiodide redox couple targeting low temperatures.
Second, an environmentally responsive hydrogel was synthesized via interpenetrating two crosslinked polymer networks. The intermolecular interactions facilitated by such an IPN structure enables not only an upper critical solution temperature (UCST) transition but also a mechanical enhancement of the hydrogel. The incorporation of functional units validates a positive swelling response to visible light and also further improves the mechanical properties. This studied IPN system can serve as a promising route in developing “smart” hydrogels utilizing visible light as a simple, inexpensive, and remotely controllable stimulus.
Over two directions across from ILs to polymeric networks, this work demonstrates an effective strategy of utilizing intermolecular interactions to not only develop multifunctional soft materials for advanced applications but also discover new properties beyond their original boundaries.
ContributorsXu, Yifei (Author) / Dai, Lenore L. (Thesis advisor) / Forzani, Erica (Committee member) / Holloway, Julianne (Committee member) / Jiang, Hanqing (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2020
Description
Limited access to clean water due to natural or municipal disasters, drought, or contaminated wells is driving demand for point-of-use and humanitarian drinking water technologies. Atmospheric water capture (AWC) can provide water off the centralized grid by capturing water vapor in ambient air and condensing it to a liquid. The overarching goal of this dissertation was to define geographic and thermodynamic design boundary conditions for AWC and develop nanotechnology-enabled AWC technologies to produce clean drinking water.
Widespread application of AWC is currently limited because water production, energy requirement, best technology, and water quality are not parameterized. I developed a geospatial climatic model for classical passive solar desiccant-driven AWC, where water vapor is adsorbed onto a desiccant bed at night, desorbed by solar heat during the day, and condensed. I concluded passive systems can capture 0.25–8 L/m2/day as a function of material properties and climate, and are limited because they only operate one adsorption-desorption-condensation cycle per day. I developed a thermodynamic model for large-scale AWC systems and concluded that the thermodynamic limit for energy to saturate and condense water vapor can vary up to 2-fold as a function of climate and mode of saturation.
Thermodynamic and geospatial models indicate opportunity space to develop AWC technologies for arid regions where solar radiation is abundant. I synthesized photothermal desiccants by optimizing surface loading of carbon black nanoparticles on micron-sized silica gel desiccants (CB-SiO2). Surface temperature of CB-SiO2 increased to 60oC under solar radiation and water vapor desorption rate was 4-fold faster than bare silica. CB-SiO2 could operate >10 AWC cycles per day to produce 2.5 L/m2/day at 40% relative humidity, 3-fold more water than a conventional passive system.
Models and bench-scale experiments were paired with pilot-scale experiments operating electrical desiccant and compressor dehumidifiers outdoors in a semi-arid climate to benchmark temporal water production, water quality and energy efficiency. Water quality varied temporally, e.g, dissolved organic carbon concentration was 3 – 12 mg/L in the summer and <1 mg/L in the winter. Collected water from desiccant systems met all Environmental Protection Agency standards, while compressor systems may require further purification for metals and turbidity.
Widespread application of AWC is currently limited because water production, energy requirement, best technology, and water quality are not parameterized. I developed a geospatial climatic model for classical passive solar desiccant-driven AWC, where water vapor is adsorbed onto a desiccant bed at night, desorbed by solar heat during the day, and condensed. I concluded passive systems can capture 0.25–8 L/m2/day as a function of material properties and climate, and are limited because they only operate one adsorption-desorption-condensation cycle per day. I developed a thermodynamic model for large-scale AWC systems and concluded that the thermodynamic limit for energy to saturate and condense water vapor can vary up to 2-fold as a function of climate and mode of saturation.
Thermodynamic and geospatial models indicate opportunity space to develop AWC technologies for arid regions where solar radiation is abundant. I synthesized photothermal desiccants by optimizing surface loading of carbon black nanoparticles on micron-sized silica gel desiccants (CB-SiO2). Surface temperature of CB-SiO2 increased to 60oC under solar radiation and water vapor desorption rate was 4-fold faster than bare silica. CB-SiO2 could operate >10 AWC cycles per day to produce 2.5 L/m2/day at 40% relative humidity, 3-fold more water than a conventional passive system.
Models and bench-scale experiments were paired with pilot-scale experiments operating electrical desiccant and compressor dehumidifiers outdoors in a semi-arid climate to benchmark temporal water production, water quality and energy efficiency. Water quality varied temporally, e.g, dissolved organic carbon concentration was 3 – 12 mg/L in the summer and <1 mg/L in the winter. Collected water from desiccant systems met all Environmental Protection Agency standards, while compressor systems may require further purification for metals and turbidity.
ContributorsMulchandani, Anjali (Author) / Westerhoff, Paul (Thesis advisor) / Rittmann, Bruce (Committee member) / Álvarez, Pedro (Committee member) / Herckes, Pierre (Committee member) / Arizona State University (Publisher)
Created2020
Description
In this work, the stacked values of battery energy storage systems (BESSs) of various power and energy capacities are evaluated as they provide multiple services such as peak shaving, frequency regulation, and reserve support in an ‘Arizona-based test system’ - a simplified, representative model of Salt River Project’s (SRP) system developed using the resource stack information shared by SRP. This has been achieved by developing a mixed-integer linear programming (MILP) based optimization model that captures the operation of BESS in the Arizona-based test system. The model formulation does not include any BESS cost as the objective is to estimate the net savings in total system operation cost after a BESS is deployed in the system. The optimization model has been formulated in such a way that the savings due to the provision of a single service, either peak shaving or frequency regulation or spinning reserve support, by the BESS, can be determined independently. The model also allows calculation of combined savings due to all the services rendered by the BESS.
The results of this research suggest that the savings obtained with a BESS providing multiple services are significantly higher than the same capacity BESS delivering a single service in isolation. It is also observed that the marginal contribution of BESS reduces with increasing BESS energy capacity, a result consistent with the law of diminishing returns. Further, small changes in the simulation environment, such as factoring in generator forced outage rates or projection of future solar penetration, can lead to changes as high as 10% in the calculated stacked value.
The results of this research suggest that the savings obtained with a BESS providing multiple services are significantly higher than the same capacity BESS delivering a single service in isolation. It is also observed that the marginal contribution of BESS reduces with increasing BESS energy capacity, a result consistent with the law of diminishing returns. Further, small changes in the simulation environment, such as factoring in generator forced outage rates or projection of future solar penetration, can lead to changes as high as 10% in the calculated stacked value.
ContributorsTripathy, Sujit Kumar (Author) / Tylavsky, Daniel J (Thesis advisor) / Pal, Anamitra (Committee member) / Wu, Meng M (Committee member) / Arizona State University (Publisher)
Created2020
Description
New communication technologies have undoubtedly altered the ways in which persons interact and have had a profound impact on public life. Engaging this impact, much of the scholarly literature has focused on how these interfaces mediate interaction however, less is known about technology's modulating effects. The current project moves beyond mediation, underscoring how social relations are not only activated by technology, but are actuated by these interfaces. Through an extended case study of Portals, gold shipping containers equipped with audio-visual technology that put persons in digital face-to-face interaction with others around the globe, the current project engages such actuation, highlighting how the co-mingling of affect and technology generate new ways of noticing, living and thinking through the complex relationships of public life. The human/technology relations mediated/modulated by the Portal produce unique atmospheres that activate/actuate public space and blur the boundaries between public and private. Additionally, the atmospheres of the Portal generate a digital co-presence that allows for user/participants to feel with their interlocutors. This “feeling with” suspends user/participants in atmospheres of human connection through the emergence of an imaginative dialogue, and the curating of such atmospheres leads to dialogic transformation. As such, the Portal operates as an atmospheric interface. Engaging the concept of atmosphere attunes those interested in new communication technologies to the complex gatherings these technologies create, and the potentialities and pitfalls of these emerging interfaces on public life.
ContributorsFerderer, Brandon Boyd (Author) / Brouwer, Daniel C. (Thesis advisor) / McHugh, Kevin (Committee member) / Hess, Aaron (Committee member) / Arizona State University (Publisher)
Created2019
Description
Due to the rapid penetration of solar power systems in residential areas, there has
been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional
power flow creates a need to know where Photovoltaic (PV) systems are
located, what their quantity is, and how much they generate. However, significant
challenges exist for accurate solar panel detection, capacity quantification,
and generation estimation by employing existing methods, because of the limited
labeled ground truth and relatively poor performance for direct supervised learning.
To mitigate these issue, this thesis revolutionizes key learning concepts to (1)
largely increase the volume of training data set and expand the labelled data set by
creating highly realistic solar panel images, (2) boost detection and quantification
learning through physical knowledge and (3) greatly enhance the generation estimation
capability by utilizing effective features and neighboring generation patterns.
These techniques not only reshape the machine learning methods in the GIS
domain but also provides a highly accurate solution to gain a better understanding
of distribution networks with high PV penetration. The numerical
validation and performance evaluation establishes the high accuracy and scalability
of the proposed methodologies on the existing solar power systems in the
Southwest region of the United States of America. The distribution and transmission
networks both have primitive control methodologies, but now is the high time
to work out intelligent control schemes based on reinforcement learning and show
that they can not only perform well but also have the ability to adapt to the changing
environments. This thesis proposes a sequence task-based learning method to
create an agent that can learn to come up with the best action set that can overcome
the issues of transient over-voltage.
been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional
power flow creates a need to know where Photovoltaic (PV) systems are
located, what their quantity is, and how much they generate. However, significant
challenges exist for accurate solar panel detection, capacity quantification,
and generation estimation by employing existing methods, because of the limited
labeled ground truth and relatively poor performance for direct supervised learning.
To mitigate these issue, this thesis revolutionizes key learning concepts to (1)
largely increase the volume of training data set and expand the labelled data set by
creating highly realistic solar panel images, (2) boost detection and quantification
learning through physical knowledge and (3) greatly enhance the generation estimation
capability by utilizing effective features and neighboring generation patterns.
These techniques not only reshape the machine learning methods in the GIS
domain but also provides a highly accurate solution to gain a better understanding
of distribution networks with high PV penetration. The numerical
validation and performance evaluation establishes the high accuracy and scalability
of the proposed methodologies on the existing solar power systems in the
Southwest region of the United States of America. The distribution and transmission
networks both have primitive control methodologies, but now is the high time
to work out intelligent control schemes based on reinforcement learning and show
that they can not only perform well but also have the ability to adapt to the changing
environments. This thesis proposes a sequence task-based learning method to
create an agent that can learn to come up with the best action set that can overcome
the issues of transient over-voltage.
ContributorsHashmy, Syed Muhammad Yousaf (Author) / Weng, Yang (Thesis advisor) / Sen, Arunabha (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Fusion proteins that specifically interact with biochemical marks on chromosomes represent a new class of synthetic transcriptional regulators that decode cell state information rather than deoxyribose nucleic acid (DNA) sequences. In multicellular organisms, information relevant to cell state, tissue identity, and oncogenesis is often encoded as biochemical modifications of histones, which are bound to DNA in eukaryotic nuclei and regulate gene expression states. In 2011, Haynes et al. showed that a synthetic regulator called the Polycomb chromatin Transcription Factor (PcTF), a fusion protein that binds methylated histones, reactivated an artificially-silenced luciferase reporter gene. These synthetic transcription activators are derived from the polycomb repressive complex (PRC) and associate with the epigenetic silencing mark H3K27me3 to reactivate the expression of silenced genes. It is demonstrated here that the duration of epigenetic silencing does not perturb reactivation via PcTF fusion proteins. After 96 hours PcTF shows the strongest reactivation activity. A variant called Pc2TF, which has roughly double the affinity for H3K27me3 in vitro, reactivated the silenced luciferase gene by at least 2-fold in living cells.
ContributorsVargas, Daniel A. (Author) / Haynes, Karmella (Thesis advisor) / Wang, Xiao (Committee member) / Mills, Jeremy (Committee member) / Arizona State University (Publisher)
Created2019