ASU Scholarship Showcase

Swinging arms are a key functional component of multistep catalytic transformations in many naturally occurring multi-enzyme complexes. This arm is typically a prosthetic chemical group that is covalently attached to the enzyme complex via a flexible linker, allowing the direct transfer of substrate molecules between multiple active sites within the complex. Mimicking this method of substrate channelling outside the cellular environment requires precise control over the spatial parameters of the individual components within the assembled complex. DNA nanostructures can be used to organize functional molecules with nanoscale precision and can also provide nanomechanical control. Until now, protein–DNA assemblies have been used to organize cascades of enzymatic reactions by controlling the relative distance and orientation of enzymatic components or by facilitating the interface between enzymes/cofactors and electrode surfaces. Here, we show that a DNA nanostructure can be used to create a multi-enzyme complex in which an artificial swinging arm facilitates hydride transfer between two coupled dehydrogenases. By exploiting the programmability of DNA nanostructures, key parameters including position, stoichiometry and inter-enzyme distance can be manipulated for optimal activity.

A structurally and compositionally well-defined and spectrally tunable artificial light-harvesting system has been constructed in which multiple organic dyes attached to a three-arm-DNA nanostructure serve as an antenna conjugated to a photosynthetic reaction center isolated from Rhodobacter sphaeroides 2.4.1. The light energy absorbed by the dye molecules is transferred to the reaction center, where charge separation takes place. The average number of DNA three-arm junctions per reaction center was tuned from 0.75 to 2.35. This DNA-templated multichromophore system serves as a modular light-harvesting antenna that is capable of being optimized for its spectral properties, energy transfer efficiency, and photostability, allowing one to adjust both the size and spectrum of the resulting structures. This may serve as a useful test bed for developing nanostructured photonic systems.

There is an increasing awareness that health care must move from post-symptomatic treatment to presymptomatic intervention. An ideal system would allow regular inexpensive monitoring of health status using circulating antibodies to report on health fluctuations. Recently, we demonstrated that peptide microarrays can do this through antibody signatures (immunosignatures). Unfortunately, printed microarrays are not scalable. Here we demonstrate a platform based on fabricating microarrays (~10 M peptides per slide, 330,000 peptides per assay) on silicon wafers using equipment common to semiconductor manufacturing. The potential of these microarrays for comprehensive health monitoring is verified through the simultaneous detection and classification of six different infectious diseases and six different cancers. Besides diagnostics, these high-density peptide chips have numerous other applications both in health care and elsewhere.

To date, little research has been performed regarding the planning and management of “small” projects – those projects typically differentiated from “large” projects due to having lower costs. In 2013, The Construction Industry Institute (CII) set out to develop a front end planning tool that will provide practitioners with a standardized process for planning small projects in the industrial sector. The research team determined that data should be sought from industry regarding small industrial projects to ensure applicability, effectiveness and validity of the new tool. The team developed and administered a survey to determine (1) the prevalence of small projects, (2) the planning processes currently in use for small projects, and (3) current metrics used by industry to differentiate between small and large projects. The survey data showed that small projects make up a majority of projects completed in the industrial sector, planning of these projects varies greatly across the industry, and the metrics posed in the survey were mostly not appropriate for use in differentiating between small and large projects. This study contributes to knowledge through adding to the limited research surrounding small projects, and suggesting future research regarding using measures of project complexity to differentiate between small and large projects.

For the past three decades, the Saudi construction industry (SCI) has exhibited poor performance. Many research efforts have tried to identify the problem and the potential causes but there have been few publications identifying ways to mitigate the problem and describing testing to validate the proposed solution. This paper examines the research and development (R&D) approach in the SCI. A literature research was performed identifying the impact that R&D has had on the SCI. A questionnaire was also created for surveying industry professionals and researchers. The results show evidence that the SCI practice and the academic research work exist in separate silos. This study recommends a change of mindset in both the public and private sector on their views on R&D since cooperation is required to create collaboration between the two sectors and improve the competitiveness of the country's economy.

The principles of a new project management model have been tested for the past 20 years. This project management model utilizes expertise instead of the traditional management, direction, and control (MDC). This new project management model is a leadership-based model instead of a management model. The practice of the new model requires a change in paradigm and project management structure. Some of the practices of this new paradigm include minimizing the flow of information and communications to and from the project manager [including meetings, emails and documents], eliminating technical communications, reducing client management, direction, and control of the vendor, and the hiring of vendors or personnel to do specific tasks. A vendors is hired only after they have clearly shown that they know what they are doing by showing past performance on similar projects, that they clearly understand how to create transparency to minimize risk that they do not control, and that they can clearly outline their project plan using a detailed milestone schedule including time, cost, and tasks all communicated in the language of metrics.

Load associated fatigue cracking is one of the major distress types occurring in flexible pavements. Flexural bending beam fatigue laboratory test has been used for several decades and is considered an integral part of the Superpave advanced characterization procedure. One of the most significant solutions to sustain the fatigue life for an asphaltic mixture is to add sustainable materials such as rubber or polymers to the asphalt mixture. A laboratory testing program was performed on three gap-graded mixtures: unmodified, Asphalt Rubber (AR) and polymer-modified. Strain controlled fatigue tests were conducted according to the AASHTO T321 procedure. The results from the beam fatigue tests indicated that the AR and polymer-modified gap graded mixtures would have much longer fatigue lives compared to the reference (unmodified) mixture. In addition, a mechanistic analysis using 3D-Move software coupled with a cost-effectiveness analysis study based on the fatigue performance on the three mixtures were performed. Overall, the analysis showed that the AR and polymer-modified asphalt mixtures exhibited significantly higher cost-effectiveness compared to unmodified HMA mixture. Although AR and polymer-modification increases the cost of the material, the analysis showed that they are more cost effective than the unmodified mixture.

As gesture interfaces become more main-stream, it is increasingly important to investigate the behavioral characteristics of these interactions – particularly in three-dimensional (3D) space. In this study, Fitts’ method was extended to such input technologies, and the applicability of Fitts’ law to gesture-based interactions was examined. The experiment included three gesture-based input devices that utilize different techniques to capture user movement, and compared them to conventional input technologies like touchscreen and mouse. Participants completed a target-acquisition test and were instructed to move a cursor from a home location to a spherical target as quickly and accurately as possible. Three distances and three target sizes were tested six times in a randomized order for all input devices. A total of 81 participants completed all tasks. Movement time, error rate, and throughput were calculated for each input technology. Results showed that the mean movement time was highly correlated with the target's index of difficulty for all devices, providing evidence that Fitts’ law can be extended and applied to gesture-based devices. Throughputs were found to be significantly lower for the gesture-based devices compared to mouse and touchscreen, and as the index of difficulty increased, the movement time increased significantly more for these gesture technologies. Error counts were statistically higher for all gesture-based input technologies compared to mouse. In addition, error counts for all inputs were highly correlated with target width, but little impact was shown by movement distance. Overall, the findings suggest that gesture-based devices can be characterized by Fitts’ law in a similar fashion to conventional 1D or 2D devices.

The relationship between sequence and binding properties of an aptamer for immunoglobulin E (IgE) was investigated using custom DNA microarrays. Single, double and some triple mutations of the aptamer sequence were created to evaluate the importance of specific base composition on aptamer binding. The majority of the positions in the aptamer sequence were found to be immutable, with changes at these positions resulting in more than a 100-fold decrease in binding affinity. Improvements in binding were observed by altering the stem region of the aptamer, suggesting that it plays a significant role in binding. Results obtained for the various mutations were used to estimate the information content and the probability of finding a functional aptamer sequence by selection from a random library. For the IgE-binding aptamer, this probability is on the order of 10^-10 to 10^-9. Results obtained for the double and triple mutations also show that there are no compensatory mutations within the space defined by those mutations. Apparently, at least for this particular aptamer, the functional sequence space can be represented as a rugged landscape with sharp peaks defined by highly constrained base compositions. This makes the rational optimization of aptamer sequences using step-wise mutagenesis approaches very challenging.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.