Synthesis and Characterization of Biomimetic Model Systems for the Study of Diiron Hydrogenase and Porphyrin Enzymes

The branch of biomimetic chemistry aims to imitate natural reactions and enzymatic processes in order to advance many other areas of chemistry, including inorganic and organic catalysis as well as materials science. To study the mechanisms of enzyme activity and the effect of secondary coordination sphere interactions, the synthesis and characterization of two artificial metallo-enzymes were pursued. In order to further understand the mechanism of [FeFe]-hydrogenase and the effect of secondary coordination sphere interactions within the protein binding pocket, the synthesis of an optimized biotin-avidin [FeFe] artificial enzyme was pursued. This dissertation describes the synthesis and characterization of the small molecule components explored for the insertion of an azadithiolato bridged [FeFe] cluster into avidin or streptavidin in order to generate a novel biotin-avidin [FeFe] hydrogenase. While further research is required in order to obtain the desired aza-bridged [FeFe] cluster compatible with insertion into avidin, the insight obtained through the attempted synthesis herein may aid others in their synthesis of [FeFe]-hydrogenase model complexes. In order to study heme enzymes, a suite of zinc porphyrin-cored random coil polymers and polymeric nanoparticles with varying degrees of potential hydrogen bonding character and steric bulk were synthesized to study secondary coordination sphere interactions. Cyanide binding studies intended to probe for hydrogen bonding environments generated by the polymer scaffold resulted in the catalytic reaction of cyanide with the solvent N,N-dimethylformamide. The reaction of cyanide with N,N-dimethylformamide in the presence of the biomimetic polymers was monitored via UV-Vis spectroscopy. The spectroscopic data lead to the determination that the collapsed topology of the polymeric nanoparticles led to higher catalytic activity than that of the random coil polymers with the fastest reactions rates occurring with polymeric nanoparticles with a greater number of potential hydrogen bond donors and larger steric bulk

Probing secondary coordination sphere interactions within porphyrin-cored polymer nanoparticles

A suite of zinc porphyrin-cored random coil polymers and polymeric nanoparticles with varying degrees of potential hydrogen bonding character and steric bulk were synthesized and characterized to study secondary coordination sphere interactions. The reaction of cyanide with N,N-dimethylformamide in the presence of porphyrin-cored polymeric nanoparticles was monitored via UV-Vis spectroscopy. It is shown that the zinc porphyrin-cored polymers and nanoparticles catalyzed the reaction of cyanide with N,N-dimethylformamide with the highest reaction rates occurring with polymeric nanoparticles with a greater number of potential hydrogen bond donors and greater steric bulk

The Lithium Depletion Boundary and the Age of the Young Open Cluster IC~2391

We have obtained new photometry and intermediate resolution ($\Delta \lambda = 2.7$ \AA\ ) spectra of 19 of these objects (14.9 $\le$ $I_c$ $\le$ 17.5) in order to confirm cluster membership. We identify 15 of our targets as likely cluster members based on their $VRI$ photometry, spectral types, radial velocity, and H$\alpha$ emission strengths. Higher S/N spectra were obtained for 8 of these probable cluster members in order to measure the strength of the lithium 6708 \AA\ doublet and thus obtain an estimate of the cluster's age. One of these 8 stars has a definite lithium detection and two other (fainter) stars have possible lithium detections. A color-magnitude diagram for our program objects shows that the lithium depletion boundary in IC~2391 is at $I_c$=16.2. Using recent theoretical model predictions, we derive an age for IC~2391 of 53$\pm$5 Myr. While this is considerably older than the age most commonly attributed for this cluster ($\sim$35 Myr) this result for IC~2391 is comparable those recently derived for the Pleiades and Alpha Persei clusters and can be explained by new models for high mass stars that incorporate a modest amount of convective core overshooting.Comment: ApJ Letters, acccepte

The development of a brief and objective method for evaluating moral sensitivity and reasoning in medical students

BACKGROUND: Most medical schools in Japan have incorporated mandatory courses on medical ethics. To this date, however, there is no established means of evaluating medical ethics education in Japan. This study looks 1) To develop a brief, objective method of evaluation for moral sensitivity and reasoning; 2) To conduct a test battery for the PIT and the DIT on medical students who are either currently in school or who have recently graduated (residents); 3) To investigate changes in moral sensitivity and reasoning between school years among medical students and residents. METHODS: Questionnaire survey: Two questionnaires were employed, the Problem Identification Test (PIT) for evaluation of moral sensitivity and a portion of the Defining Issues Test (DIT) for moral reasoning. Subjects consisted of 559 medical school students and 272 residents who recently graduated from the same medical school located in an urban area of Japan. RESULTS: PIT results showed an increase in moral sensitivity in 4(th )and 5(th )year students followed by a decrease in 6(th )year students and in residents. No change in moral development stage was observed. However, DIT results described a gradual rising shift in moral decision-making concerning euthanasia between school years. No valid correlation was observed between PIT and DIT questionnaires. CONCLUSION: This study's questionnaire survey, which incorporates both PIT and DIT, could be used as a brief and objective means of evaluating medical students' moral sensitivity and reasoning in Japan

Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder.

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.PEV was supported by the Medical Research Council (grant no. MR/K020706/1) and is a Fellow of MQ: Transforming Mental Health (MQF17_24)

Bayesian Statistical Models of Shape and Appearance for Subcortical Brain Segmentation

Our motivation is to develop an automated technique for the segmentation of subcortical human brain structures from MR images. To this purpose, models of shapeand- appearance are constructed and fit to new image data. The statistical models are trained from 317 manually labelled T1-weighted MR images. Shape is modelled using a surface-based point distribution model (PDM) such that the shape space is constrained to the linear combination of the mean shape and eigenvectors of the vertex coordinates. In addition, to model intensity at the structural boundary, intensities are sampled along the surface normal from the underlying image. We propose a novel Bayesian appearance model whereby the relationship between shape and intensity are modelled via the conditional distribution of intensity given shape. Our fully probabilistic approach eliminates the need for arbitrary weightings between shape and intensity as well as for tuning parameters that specify the relative contribution between the use of shape constraints and intensity information. Leave-one-out crossvalidation is used to validate the model and fitting for 17 structures. The PDM for shape requires surface parameterizations of the volumetric, manual labels such that vertices retain a one-to-one correspondence across the training subjects. Surface parameterizations with correspondence are generated through the use of deformable models under constraints that embed the correspondence criterion within the deformation process. A novel force that favours equal-area triangles throughout the mesh is introduced. The force adds stability to the mesh such that minimal smoothing or within-surface motion is required. The use of the PDM for segmentation across a series of subjects results in a set surfaces that retain point correspondence. The correspondence facilitates landmarkbased shape analysis. Amongst other metrics, vertex-wise multivariate statistics and discriminant analysis are used to investigate local and global size and shape differences between groups. The model is fit, and shape analysis is applied to two clinical datasets.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bayesian statistical models of shape and appearance for subcortical brain segmentation

Our motivation is to develop an automated technique for the segmentation of sub-cortical human brain structures from MR images. To this purpose, models of shape-and-appearance are constructed and fit to new image data. The statistical models are trained from 317 manually labelled T1-weighted MR images. Shape is modelled using a surface-based point distribution model (PDM) such that the shape space is constrained to the linear combination of the mean shape and eigenvectors of the vertex coordinates. In addition, to model intensity at the structural boundary, intensities are sampled along the surface normal from the underlying image. We propose a novel Bayesian appearance model whereby the relationship between shape and intensity are modelled via the conditional distribution of intensity given shape. Our fully probabilistic approach eliminates the need for arbitrary weightings between shape and intensity as well as for tuning parameters that specify the relative contribution between the use of shape constraints and intensity information. Leave-one-out cross-validation is used to validate the model and fitting for 17 structures.The PDM for shape requires surface parameterizations of the volumetric, manual labels such that vertices retain a one-to-one correspondence across the training subjects. Surface parameterizations with correspondence are generated through the use of deformable models under constraints that embed the correspondence criterion within the deformation process. A novel force that favours equal-area triangles throughout the mesh is introduced. The force adds stability to the mesh such that minimal smoothing or within-surface motion is required.The use of the PDM for segmentation across a series of subjects results in a set surfaces that retain point correspondence. The correspondence facilitates landmark-based shape analysis. Amongst other metrics, vertex-wise multivariate statistics and discriminant analysis are used to investigate local and global size and shape differences between groups. The model is fit, and shape analysis is applied to two clinical datasets.This thesis is not currently available via ORA

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