Filtering by
- Member of: Programs and Communities
- Member of: ASU Electronic Theses and Dissertations
The XFEL is characterized by high intensity pulses, which are only about 50 femtoseconds in duration. The intensity allows for scattering from microscopic particles, while the short pulses offer a way to outrun radiation damage. XFELs are powerful enough to obliterate most samples in a single pulse. While this allows for a “diffract and destroy” methodology, it also requires instrumentation that can position microscopic particles into the X-ray beam (which may also be microscopic), continuously renew the sample after each pulse, and maintain sample viability during data collection.
Typically these experiments have used liquid microjets to continuously renew sample. The high flow rate associated with liquid microjets requires large amounts of sample, most of which runs to waste between pulses. An injector designed to stream a viscous gel-like material called lipidic cubic phase (LCP) was developed to address this problem. LCP, commonly used as a growth medium for membrane protein crystals, lends itself to low flow rate jetting and so reduces the amount of sample wasted significantly.
This work discusses sample delivery and injection for XFEL experiments. It reviews the liquid microjet method extensively, and presents the LCP injector as a novel device for serial crystallography, including detailed protocols for the LCP injector and anti-settler operation.
(XFEL) allows it to outrun radiation damage in coherent diffractive imaging since elastic scattering terminates before photoelectron cascades commences. This “diffract-before-destroy” feature of XFEL opened up new opportunities for biological macromolecule imaging and structure studies by breaking the limit to spatial resolution imposed by the maximum dose that is allowed before radiation damage. However, data collection in serial femto-second crystallography (SFX) using XFEL is affected by a bunch of stochastic factors, which pose great challenges to the data analysis in SFX. These stochastic factors include crystal size, shape, random orientation, X-ray photon flux, position and energy spectrum. Monte-Carlo integration proves effective and successful in extracting the structure factors by merging all diffraction patterns given that the data set is sufficiently large to average out all stochastic factors. However, this approach typically requires hundreds of thousands of patterns collected from experiments. This dissertation explores both experimental and algorithmic methods to eliminate or reduce the effect of stochastic factors in data acquisition and analysis. Coherent convergent X-ray beam diffraction (CCB) is discussed for possibilities of obtaining single-shot angular-integrated rocking curves. It is also shown the interference between Bragg disks helps ab-initio phasing. Two-color diffraction scheme is proposed for time-resolved studies and general data collection strategies are discussed based on error metrics. A new auto-indexing algorithm for sparse patterns is developed and demonstrated for both simulated and experimental data. Statistics show that indexing rate is increased by 3 times for I3C data set collected from beam time LJ69 at Linac coherent light source (LCLS). Finally, dynamical inversion from electron diffraction is explored as an alternative approach for structure determination.
It starts with establishing the limitations of traditional electron diffraction coupled with molecular replacement to study biomolecular structure and proceeds to suggest a pulsed electron source Hollow-Cone Transmission Electron Microscope as an alternative scheme to pursue ultrafast biomolecular imaging. In frequency domain, the use of Electron Energy Loss Spectroscopy as a tool to access ultrafast nuclear dynamics in the steady state, is detailed with the new monochromated NiON UltraSTEM and examples demonstrating this instrument’s capability are provided.
Ultrafast X-ray spectroscopy as a tool to elucidate biomolecular dynamics is presented in studying X-ray as a probe, with the study of the photolysis of Methylcobalamin using time-resolved laser pump – X-ray probe absorption spectroscopy. The analysis in comparison to prior literature as well as DFT based XAS simulations offer good agreement and understanding to the steady state spectra but are so far inadequate in explaining the time-resolved data. However, the trends in the absorption simulations for the transient intermediates show a strong anisotropic dependence on the axial ligation, which would define the direction for future studies on this material to achieve a solution.
Briefly explains how lack of monetary savings serves as a barrier to accessing to finance capital for women of color seeking to launch their own tech startup.
Background:
Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.
Results:
We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.
Conclusions:
The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.
expenditure, and environmental risk. Surfactant treatment to disrupt Scenedesmus biomass was evaluated
as a means to make solvent extraction more efficient. Surfactant treatment increased the recovery of fatty
acid methyl ester (FAME) by as much as 16-fold vs. untreated biomass using isopropanol extraction, and
nearly 100% FAME recovery was possible without any Folch solvent, which is toxic and expensive. Surfactant
treatment caused cell disruption and morphological changes to the cell membrane, as documented by
transmission electron microscopy and flow cytometry. Surfactant treatment made it possible to extract wet
biomass at room temperature, which avoids the expense and energy cost associated with heating
and drying of biomass during the extraction process. The best FAME recovery was obtained from highlipid
biomass treated with Myristyltrimethylammonium bromide (MTAB)- and 3-(decyldimethylammonio)-
propanesulfonate inner salt (3_DAPS)-surfactants using a mixed solvent (hexane : isopropanol = 1 : 1, v/v)
vortexed for just 1 min; this was as much as 160-fold higher than untreated biomass. The critical micelle
concentration of the surfactants played a major role in dictating extraction performance, but the growth
stage of the biomass had an even larger impact on how well the surfactants disrupted the cells and
improved lipid extraction. Surfactant treatment had minimal impact on extracted-FAME profiles and,
consequently, fuel-feedstock quality. This work shows that surfactant treatment is a promising strategy for
more efficient, sustainable, and economical extraction of fuel feedstock from microalgae.