Matching Items (32)
Description
Scientific research encompasses a variety of objectives, including measurement, making predictions, identifying laws, and more. The advent of advanced measurement technologies and computational methods has largely automated the processes of big data collection and prediction. However, the discovery of laws, particularly universal ones, still heavily relies on human intellect. Even with human intelligence, complex systems present a unique challenge in discerning the laws that govern them. Even the preliminary step, system description, poses a substantial challenge. Numerous metrics have been developed, but universally applicable laws remain elusive. Due to the cognitive limitations of human comprehension, a direct understanding of big data derived from complex systems is impractical. Therefore, simplification becomes essential for identifying hidden regularities, enabling scientists to abstract observations or draw connections with existing knowledge. As a result, the concept of macrostates -- simplified, lower-dimensional representations of high-dimensional systems -- proves to be indispensable. Macrostates serve a role beyond simplification. They are integral in deciphering reusable laws for complex systems. In physics, macrostates form the foundation for constructing laws and provide building blocks for studying relationships between quantities, rather than pursuing case-by-case analysis. Therefore, the concept of macrostates facilitates the discovery of regularities across various systems. Recognizing the importance of macrostates, I propose the relational macrostate theory and a machine learning framework, MacroNet, to identify macrostates and design microstates. The relational macrostate theory defines a macrostate based on the relationships between observations, enabling the abstraction from microscopic details. In MacroNet, I propose an architecture to encode microstates into macrostates, allowing for the sampling of microstates associated with a specific macrostate. My experiments on simulated systems demonstrate the effectiveness of this theory and method in identifying macrostates such as energy. Furthermore, I apply this theory and method to a complex chemical system, analyzing oil droplets with intricate movement patterns in a Petri dish, to answer the question, ``which combinations of parameters control which behavior?'' The macrostate theory allows me to identify a two-dimensional macrostate, establish a mapping between the chemical compound and the macrostate, and decipher the relationship between oil droplet patterns and the macrostate.
ContributorsZhang, Yanbo (Author) / Walker, Sara I (Thesis advisor) / Anbar, Ariel (Committee member) / Daniels, Bryan (Committee member) / Das, Jnaneshwar (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2023
Description
Much attention has been given to the behavior of quantum fields in expanding Freidmann-Lema\^itre-Robertson-Walker (FLRW) spacetimes, and de Sitter spacetime in particular. In such spacetimes, the S-matrix is ill-defined, so new observables must be constructed that are accessible to both computation and measurement. The most common observable in theories of inflation is an equal-time correlation function, typically computed in the in-in formalism. Weinberg improved upon in-in perturbation theory by reducing the perturbative expansion to a series of nested commutators. Several authors noted a technical difference between Weinberg's formula and standard in-in perturbation theory. In this work, a proof of the order-by-order equivalence of Weinberg's commutators to traditional in-in perturbation theory is presented for all masses and commonly studied spins in a broad class of FLRW spacetimes. Then, a study of the effects of a sector of conformal matter coupled solely to gravity is given. The results can constrain N-naturalness as a complete solution of the hierarchy problem, given a measurement of the tensor fluctuations from inflation. The next part of this work focuses on the thermodynamics of de Sitter. It has been known for decades that there is a temperature associated with a cosmological horizon, which matches the thermal response of a comoving particle detector in de Sitter. A model of a perfectly reflecting cavity is constructed with fixed physical size in two-dimensional de Sitter spacetime. The natural ground state inside the box yields no response from a comoving particle detector, implying that the box screens out the thermal effects of the de Sitter horizon. The total energy inside the box is also shown to be smaller than an equivalent volume of the Bunch-Davies vacuum state. The temperature difference across the wall of the box might drive a heat engine, so an analytical model of the Szil\'ard engine is constructed and studied. It is found that all relevant thermodynamical quantities can be computed exactly at all stages of the engine cycle.
ContributorsThomas, Logan (Author) / Baumgart, Matthew (Thesis advisor) / Davies, Paul (Committee member) / Easson, Damien (Committee member) / Keeler, Cynthia (Committee member) / Arizona State University (Publisher)
Created2023
Description
Relapse after tumor dormancy is one of the leading causes of cancer recurrence that ultimately leads to patient mortality. Upon relapse, cancer manifests as metastases that are linked to almost 90% cancer related deaths. Capture of the dormant and relapsed tumor phenotypes in high-throughput will allow for rapid targeted drug discovery, development and validation. Ablation of dormant cancer will not only completely remove the cancer disease, but also will prevent any future recurrence. A novel hydrogel, Amikagel, was developed by crosslinking of aminoglycoside amikacin with a polyethylene glycol crosslinker. Aminoglycosides contain abundant amount of easily conjugable groups such as amino and hydroxyl moieties that were crosslinked to generate the hydrogel. Cancer cells formed 3D spheroidal structures that underwent near complete dormancy on Amikagel high-throughput drug discovery platform. Due to their dormant status, conventional anticancer drugs such as mitoxantrone and docetaxel that target the actively dividing tumor phenotype were found to be ineffective. Hypothesis driven rational drug discovery approaches were used to identify novel pathways that could sensitize dormant cancer cells to death. Strategies were used to further accelerate the dormant cancer cell death to save time required for the therapeutic outcome.
Amikagel’s properties were chemo-mechanically tunable and directly impacted the outcome of tumor dormancy or relapse. Exposure of dormant spheroids to weakly stiff and adhesive formulation of Amikagel resulted in significant relapse, mimicking the response to changes in extracellular matrix around dormant tumors. Relapsed cells showed significant differences in their metastatic potential compared to the cells that remained dormant after the induction of relapse. Further, the dissertation discusses the use of Amikagels as novel pDNA binding resins in microbead and monolithic formats for potential use in chromatographic purifications. High abundance of amino groups allowed their utilization as novel anion-exchange pDNA binding resins. This dissertation discusses Amikagel formulations for pDNA binding, metastatic cancer cell separation and novel drug discovery against tumor dormancy and relapse.
Amikagel’s properties were chemo-mechanically tunable and directly impacted the outcome of tumor dormancy or relapse. Exposure of dormant spheroids to weakly stiff and adhesive formulation of Amikagel resulted in significant relapse, mimicking the response to changes in extracellular matrix around dormant tumors. Relapsed cells showed significant differences in their metastatic potential compared to the cells that remained dormant after the induction of relapse. Further, the dissertation discusses the use of Amikagels as novel pDNA binding resins in microbead and monolithic formats for potential use in chromatographic purifications. High abundance of amino groups allowed their utilization as novel anion-exchange pDNA binding resins. This dissertation discusses Amikagel formulations for pDNA binding, metastatic cancer cell separation and novel drug discovery against tumor dormancy and relapse.
ContributorsGrandhi, Taraka Sai Pavan (Author) / Rege, Kaushal (Thesis advisor) / Meldrum, Deirdre R (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Caplan, Michael (Committee member) / Tian, Yanqing (Committee member) / Arizona State University (Publisher)
Created2016
Description
Within the last decade there has been remarkable interest in single-cell metabolic analysis as a key technology for understanding cellular heterogeneity, disease initiation, progression, and drug resistance. Technologies have been developed for oxygen consumption rate (OCR) measurements using various configurations of microfluidic devices. The technical challenges of current approaches include: (1) deposition of multiple sensors for multi-parameter metabolic measurements, e.g. oxygen, pH, etc.; (2) tedious and labor-intensive microwell array fabrication processes; (3) low yield of hermetic sealing between two rigid fused silica parts, even with a compliance layer of PDMS or Parylene-C. In this thesis, several improved microfabrication technologies are developed and demonstrated for analyzing multiple metabolic parameters from single cells, including (1) a modified "lid-on-top" configuration with a multiple sensor trapping (MST) lid which spatially confines multiple sensors to micro-pockets enclosed by lips for hermetic sealing of wells; (2) a multiple step photo-polymerization method for patterning three optical sensors (oxygen, pH and reference) on fused silica and on a polyethylene terephthalate (PET) surface; (3) a photo-polymerization method for patterning tri-color (oxygen, pH and reference) optical sensors on both fused silica and on the PET surface; (4) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can withstand cell culture conditions. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.
ContributorsSong, Ganquan (Author) / Meldrum, Deirdre R (Thesis advisor) / Goryll, Michael (Committee member) / Wang, Hong (Committee member) / Tian, Yanqing (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis explores the different aspects of higher curvature gravity. The "membrane paradigm" of black holes in Einstein gravity is extended to black holes in f(R) gravity and it is shown that the higher curvature effects of f(R) gravity causes the membrane fluid to become non-Newtonian. Next a modification of the null energy condition in gravity is provided. The purpose of the null energy condition is to filter out ill-behaved theories containing ghosts. Conformal transformations, which are simple redefinitions of the spacetime, introduces serious violations of the null energy condition. This violation is shown to be spurious and a prescription for obtaining a modified null energy condition, based on the universality of the second law of thermodynamics, is provided. The thermodynamic properties of the black holes are further explored using merger of extremal black holes whose horizon entropy has topological contributions coming from the higher curvature Gauss-Bonnet term. The analysis refutes the prevalent belief in the literature that the second law of black hole thermodynamics is violated in the presence of the Gauss-Bonnet term in four dimensions. Subsequently a specific class of higher derivative scalar field theories called the galileons are obtained from a Kaluza-Klein reduction of Gauss-Bonnet gravity. Galileons are null energy condition violating theories which lead to violations of the second law of thermodynamics of black holes. These higher derivative scalar field theories which are non-minimally coupled to gravity required the development of a generalized method for obtaining the equations of motion. Utilizing this generalized method, it is shown that the inclusion of the Gauss-Bonnet term made the theory of gravity to become higher derivative, which makes it difficult to make any statements about the connection between the violation of the second law of thermodynamics and the galileon fields.
ContributorsChatterjee, Saugata (Author) / Parikh, Maulik K (Thesis advisor) / Easson, Damien (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2014
Description
Traumatic brain injury (TBI) is a leading cause of disability worldwide with 1.7 million TBIs reported annually in the United States. Broadly, TBI can be classified into focal injury, associated with cerebral contusion, and diffuse injury, a widespread injury pathology. TBI results in a host of pathological alterations and may lead to a transient blood-brain-barrier (BBB) breakdown. Although the BBB dysfunction after TBI may provide a window for therapeutic delivery, the current drug delivery approaches remains largely inefficient due to rapid clearance, inactivation and degradation. One potential strategy to address the current therapeutic limitations is to employ nanoparticle (NP)-based technology to archive greater efficacy and reduced clearance compared to standard drug administration. However, NP application for TBI is challenging not only due to the transient temporal resolution of the BBB breakdown, but also due to the heterogeneous (focal/diffuse) aspect of the disease itself. Furthermore, recent literature suggests sex of the animal influences neuroinflammation/outcome after TBI; yet, the influence of sex on BBB integrity following TBI and subsequent NP delivery has not been previously investigated. The overarching hypothesis for this thesis is that TBI-induced compromised BBB and leaky vasculature will enable delivery of systemically injected NPs to the injury penumbra. This study specifically explored the feasibility and the temporal accumulation of NPs in preclinical mouse models of focal and diffuse TBI. Key findings from these studies include the following. (1) After focal TBI, NPs ranging from 20-500nm exhibited peak accumulation within the injury penumbra acutely (1h) post-injury. (2) A smaller delayed peak of NP accumulation (40nm) was observed sub-acutely (3d) after focal brain injury. (3) Mild diffuse TBI simulated with a mild closed head injury model did not display any measurable NP accumulation after 1h post-injury. (4) In contrast, a moderate diffuse model (fluid percussion injury) demonstrated peak accumulation at 3h post-injury with up to 500 nm size NPs accumulating in cortical tissue. (5) Robust NP accumulation (40nm) was found in female mice compared to the males at 24h and 3d following focal brain injury. Taken together, these results demonstrate the potential for NP delivery at acute and sub-acute time points after TBI by exploiting the compromised BBB. Results also reveal a potential sex dependent component of BBB disruption leading to altered NP accumulation. The applications of this research are far-reaching ranging from theranostic delivery to personalized NP delivery for effective therapeutic outcome.
ContributorsBharadwaj, Vimala Nagabhushana (Author) / Stabenfeldt, Sarah E (Thesis advisor) / Kodibagkar, Vikram D (Thesis advisor) / Kleim, Jeffrey (Committee member) / Tian, Yanqing (Committee member) / Lifshitz, Jonathan (Committee member) / Anderson, Trent R (Committee member) / Arizona State University (Publisher)
Created2018
Description
A swarm describes a group of interacting agents exhibiting complex collective behaviors. Higher-level behavioral patterns of the group are believed to emerge from simple low-level rules of decision making at the agent-level. With the potential application of swarms of aerial drones, underwater robots, and other multi-robot systems, there has been increasing interest in approaches for specifying complex, collective behavior for artificial swarms. Traditional methods for creating artificial multi-agent behaviors inspired by known swarms analyze the underlying dynamics and hand craft low-level control logics that constitute the emerging behaviors. Deep learning methods offered an approach to approximate the behaviors through optimization without much human intervention.
This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.
This thesis proposes a graph based neural network architecture, SwarmNet, for learning the swarming behaviors of multi-agent systems. Given observation of only the trajectories of an expert multi-agent system, the SwarmNet is able to learn sensible representations of the internal low-level interactions on top of being able to approximate the high-level behaviors and make long-term prediction of the motion of the system. Challenges in scaling the SwarmNet and graph neural networks in general are discussed in detail, along with measures to alleviate the scaling issue in generalization is proposed. Using the trained network as a control policy, it is shown that the combination of imitation learning and reinforcement learning improves the policy more efficiently. To some extent, it is shown that the low-level interactions are successfully identified and separated and that the separated functionality enables fine controlled custom training.
ContributorsZhou, Siyu (Author) / Ben Amor, Heni (Thesis advisor) / Walker, Sara I (Thesis advisor) / Davies, Paul (Committee member) / Pavlic, Ted (Committee member) / Presse, Steve (Committee member) / Arizona State University (Publisher)
Created2020
Description
This work suggests an effective approach to fabricate reduced graphene oxide-based carbon (RGO/C) composite films. The carbonization of graphene oxide-reinforced polyimide (GO/PI) composite films was investigated by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The crystalline structures and carbonized mechanism of the RGO/C composite films were investigated in detail by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Furthermore, the carbonization yields were improved due to the catalytic effects of RGO. These RGO/C composite films exhibited obvious structural orientations by SEM investigation of their cross sections.
ContributorsNiu, Yongan (Author) / Zhang, Xin (Author) / Zhao, Jiupeng (Author) / Tian, Yanqing (Author) / Yan, Xiangqiao (Author) / Li, Yao (Author) / Biodesign Institute (Contributor)
Created2014-04-11
Description
Although emerging evidence indicates that deep-sea water contains an untapped reservoir of high metabolic and genetic diversity, this realm has not been studied well compared with surface sea water. The study provided the first integrated meta-genomic and -transcriptomic analysis of the microbial communities in deep-sea water of North Pacific Ocean. DNA/RNA amplifications and simultaneous metagenomic and metatranscriptomic analyses were employed to discover information concerning deep-sea microbial communities from four different deep-sea sites ranging from the mesopelagic to pelagic ocean. Within the prokaryotic community, bacteria is absolutely dominant (~90%) over archaea in both metagenomic and metatranscriptomic data pools. The emergence of archaeal phyla Crenarchaeota, Euryarchaeota, Thaumarchaeota, bacterial phyla Actinobacteria, Firmicutes, sub-phyla Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria, and the decrease of bacterial phyla Bacteroidetes and Alphaproteobacteria are the main composition changes of prokaryotic communities in the deep-sea water, when compared with the reference Global Ocean Sampling Expedition (GOS) surface water. Photosynthetic Cyanobacteria exist in all four metagenomic libraries and two metatranscriptomic libraries. In Eukaryota community, decreased abundance of fungi and algae in deep sea was observed. RNA/DNA ratio was employed as an index to show metabolic activity strength of microbes in deep sea. Functional analysis indicated that deep-sea microbes are leading a defensive lifestyle.
ContributorsWu, Jieying (Author) / Gao, Weimin (Author) / Johnson, Roger (Author) / Zhang, Weiwen (Author) / Meldrum, Deirdre (Author) / Biodesign Institute (Contributor)
Created2013-10-11
Description
Core-shell microgels containing sensors/dyes in a matrix were fabricated by two-stage free radical precipitation polymerization method for ratiometric sensing/imaging. The microgels composing of poly(N-isopropylacrylamide) (PNIPAm) shell exhibits a low critical solution temperature (LCST), underwent an entropically driven transition from a swollen state to a deswollen state, which exhibit a hydrodynamic radius of ∼450 nm at 25°C (in vitro) and ∼190 nm at 37°C (in vivo). The microgel’s ability of escaping from lysosome into cytosol makes the microgel be a potential candidate for cytosolic delivery of sensors/probes. Non-invasive imaging/sensing in Antigen-presenting cells (APCs) was feasible by monitoring the changes of fluorescence intensity ratios. Thus, these biocompatible microgels-based imaging/sensing agents may be expected to expand current molecular imaging/sensing techniques into methods applicable to studies in vivo, which could further drive APC-based treatments.
ContributorsZhou, Xianfeng (Author) / Su, Fengyu (Author) / Tian, Yanqing (Author) / Meldrum, Deirdre (Author) / Biodesign Institute (Contributor)
Created2014-02-04