Statistical learning for alloy design from electronic structure calculations

The objective of this thesis is to explore how statistical learning methods can contribute to the interpretation and efficacy of electronic structure calculations. This study develops new applications of statistical learning and data mining methods to both semi-empirical and density functional theory (DFT) calculations. Each of these classes of electronic structure calculations serves as templates for different data driven discovery strategies for materials science applications. In our study of semi-empirical methods, we take advantage of the ability of data mining methods to quantitatively assess high dimensional parameterization schemes. The impact of this work includes the development of accelerated computational schemes for developing reduced order models. Another application is the use of these informatics based techniques to serve as a means for estimating parameters when data for such calculations are not available. Using density of states (DOS) spectra derived from DFT calculations we have demonstrated the classification power of singular value decomposition methods to accurately develop structural and stoichiometric classifications of compounds. Building on this work we have extended this analytical strategy to apply the predictive capacity of informatics methods to develop a new and far more robust modeling approach for DOS spectra, addressing an issue that has gone relatively unchallenged over two decades. By exploring a diverse array of materials systems (metals, ceramics, different crystal structures) this work has laid the foundations for expanding the linkages between statistical learning and statistical thermodynamics. The results of this work provide exciting new opportunities in computational based design of materials that have not been explored before

Tracking chemical processing pathways in combinatorial polymer libraries via data mining

Changes in the molecular structure and composition of interpenetrating polymer networks (IPNs) can be used to tailor their properties. While the properties of IPNs are typically different than polymer blends, a clear understanding of the impact of changing polymerization sequence on the physical properties and the corresponding molecular bonding is needed. To address this issue, a data mining approach is used to identify the change with polymerization sequence of tensile and rheological properties of acrylate-epoxy IPNs. The experimental approach used to study the molecular structure is high throughput Fourier transform infrared (FTIR) spectroscopy. Analysis of the FTIR spectra of IPNs synthesized with different polymerization sequences leads to an understanding of the molecular bonding responsible for the tensile and rheological properties. From the interpretation of the wavenumber bands and associated molecular bonds, we find that the polymerization sequence most affects hydrogen bonding and aromatic ring bond energies. This work defines the relationships between chemistry, structure, processing, and properties of the IPN samples

Identifying factors controlling protein release from combinatorial biomaterial libraries via hybrid data mining methods

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO- nonconjugated bonds, the number of aromatic rings, and the number of -CH 2- bonds

A data analytics approach for rational design of nanomedicines with programmable drug release

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions between) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and a large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Non-linear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in training data set. The models were shown to be robust after inclusion of these new formulations in that the new inclusions did not significantly change model regression. This approach provides the first steps towards development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics

Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives

Clinically relevant dual probe difference specimen imaging (DDSI) protocol for freshly resected breast cancer specimen staining

Background: Re-excision rates following breast conserving surgery (BCS) remain as high as ~ 35%, with positive margins detected during follow-up histopathology. Additional breast cancer resection surgery is not only taxing on the patient and health care system, but also delays adjuvant therapies, increasing morbidity and reducing the likelihood of a positive outcome. The ability to precisely resect and visualize tumor margins in real time within the surgical theater would greatly benefit patients, surgeons and the health care system. Current tumor margin assessment technologies utilized during BCS involve relatively lengthy and labor-intensive protocols, which impede the surgical work flow. Methods: In previous work, we have developed and validated a fluorescence imaging method termed dual probe difference specimen imaging (DDSI) to accurately detect benign and malignant tissue with direct correlation to the targeted biomarker expression levels intraoperatively. The DDSI method is currently on par with touch prep cytology in execution time (~ 15-min). In this study, the main goal was to shorten the DDSI protocol by decreasing tissue blocking and washing times to optimize the DDSI protocol to \u3c 10-min whilst maintaining robust benign and malignant tissue differentiation. Results: We evaluated the utility of the shortened DDSI staining methodology using xenografts grown from cell lines with varied epidermal growth factor receptor (EGFR) expression levels, comparing accuracy through receiver operator characteristic (ROC) curve analyses across varied tissue blocking and washing times. An optimized 8-min DDSI methodology was developed for future clinical translation. Conclusions: Successful completion of this work resulted in substantial shortening of the DDSI methodology for use in the operating room, that provided robust, highly receptor specific, sensitive diagnostic capabilities between benign and malignant tissues

A systems approach to designing next generation vaccines: Combining α-galactose modified antigens with nanoparticle platforms

Innovative vaccine platforms are needed to develop effective countermeasures against emerging and re-emerging diseases. These platforms should direct antigen internalization by antigen presenting cells and promote immunogenic responses. This work describes an innovative systems approach combining two novel platforms, αGalactose (αGal)-modification of antigens and amphiphilic polyanhydride nanoparticles as vaccine delivery vehicles, to rationally design vaccine formulations. Regimens comprising soluble αGal-modified antigen and nanoparticle-encapsulated unmodified antigen induced a high titer, high avidity antibody response with broader epitope recognition of antigenic peptides than other regimen. Proliferation of antigen-specific CD4 + T cells was also enhanced compared to a traditional adjuvant. Combining the technology platforms and augmenting immune response studies with peptide arrays and informatics analysis provides a new paradigm for rational, systems-based design of next generation vaccine platforms against emerging and re-emerging pathogens

Binge flying: Behavioural addiction and climate change

Recent popular press suggests that ‘binge flying’ constitutes a new site of behavioural addiction. We theoretically appraise and empirically support this proposition through interviews with consumers in Norway and the United Kingdom conducted in 2009. Consistent findings from across two national contexts evidence a growing negative discourse towards frequent short-haul tourist air travel and illustrate strategies of guilt suppression and denial used to span a cognitive dissonance between the short-term personal benefits of tourism and the air travel’s associated long-term consequences for climate change. Tensions between tourism consumption and changing social norms towards acceptable flying practice exemplify how this social group is beginning to (re)frame what constitutes ‘excessive’ holiday flying, despite concomitantly continuing their own frequent air travels

RNA Nanovaccine Protects against White Spot Syndrome Virus in Shrimp

In the last 15 years, crustacean fisheries have experienced billions of dollars in economic losses, primarily due to viral diseases caused by such pathogens as white spot syndrome virus (WSSV) in the Pacific white shrimp Litopenaeus vannamei and Asian tiger shrimp Penaeus monodon. To date, no effective measures are available to prevent or control disease outbreaks in these animals, despite their economic importance. Recently, double-stranded RNA-based vaccines have been shown to provide specific and robust protection against WSSV infection in cultured shrimp. However, the limited stability of double-stranded RNA is the most significant hurdle for the field application of these vaccines with respect to delivery within an aquatic system. Polyanhydride nanoparticles have been successfully used for the encapsulation and release of vaccine antigens. We have developed a double-stranded RNA-based nanovaccine for use in shrimp disease control with emphasis on the Pacific white shrimp L. vannamei. Nanoparticles based on copolymers of sebacic acid, 1,6-bis(pcarboxyphenoxy) hexane, and 1,8-bis(p-carboxyphenoxy)-3,6-dioxaoctane exhibited excellent safety profiles, as measured by shrimp survival and histological evaluation. Furthermore, the nanoparticles localized to tissue target replication sites for WSSV and persisted through 28 days postadministration. Finally, the nanovaccine provided ~80% protection in a lethal WSSV challenge model. This study demonstrates the exciting potential of a safe, effective, and field-applicable RNA nanovaccine that can be rationally designed against infectious diseases affecting aquaculture