Calphad Modelling of Ceramic Systems

Thermodynamic modeling based on the CALPHAD approach is a powerful tool for understanding the behavior and designing materials with optimized properties. This method relies on the availability of relevant Gibbs energy functions. The present work is concerned with developing the Gibbs energy functions for Si-Zr-N and Ti-Zr-N systems. The outcome of implementing such an approach is a set of internally consistent Gibbs energy functions for various phases. These functions are used to build Gibbs energy databases for multicomponent systems that are accessed by Gibbs energy minimization software to compute phase diagrams and thermochemical properties. They can be used to compute the thermochemical and constitutional information that would help in understanding the behavior of these materials and optimizing their compositions for different applications

Phenomenology of a-axis and b-axis charge dynamics from microwave spectroscopy of highly ordered YBa2Cu3O6.50 and YBa2Cu3O6.993

Extensive measurements of the microwave conductivity of highly pure and oxygen-ordered \YBCO single crystals have been performed as a means of exploring the intrinsic charge dynamics of a d-wave superconductor. Broadband and fixed-frequency microwave apparatus together provide a very clear picture of the electrodynamics of the superconducting condensate and its thermally excited nodal quasiparticles. The measurements reveal the existence of very long-lived excitations deep in the superconducting state, as evidenced by sharp cusp-like conductivity spectra with widths that fall well within our experimental bandwidth. We present a phenomenological model of the microwave conductivity that captures the physics of energy-dependent quasiparticle dynamics in a d-wave superconductor which, in turn, allows us to examine the scattering rate and oscillator strength of the thermally excited quasiparticles as functions of temperature. Our results are in close agreement with the Ferrell-Glover-Tinkham sum rule, giving confidence in both our experiments and the phenomenological model. Separate experiments for currents along the $\hat a$ and $\hat b$ directions of detwinned crystals allow us to isolate the role of the CuO chain layers in \YBCO, and a model is presented that incorporates both one-dimensional conduction from the chain electrons and two-dimensional transport associated with the \cuplane plane layers.Comment: 17 pages, 13 figure

Substrate Activation and Conformational Dynamics of Guanosine 5′-Monophosphate Synthetase

Glutamine amidotransferases catalyze the amination of a wide range of molecules using the amide nitrogen of glutamine. The family provides numerous examples for study of multi-active-site regulation and interdomain communication in proteins. Guanosine 5′-monophosphate synthetase (GMPS) is one of three glutamine amidotransferases in <i>de novo</i> purine biosynthesis and is responsible for the last step in the guanosine branch of the pathway, the amination of xanthosine 5′-monophosphate (XMP). In several amidotransferases, the intramolecular path of ammonia from glutamine to substrate is understood; however, the crystal structure of GMPS only hinted at the details of such transfer. Rapid kinetics studies provide insight into the mechanism of the substrate-induced changes in this complex enzyme. Rapid mixing of GMPS with substrates also manifests absorbance changes that report on the kinetics of formation of a reactive intermediate as well as steps in the process of rapid transfer of ammonia to this intermediate. Isolation and use of the adenylylated nucleotide intermediate allowed the study of the amido transfer reaction distinct from the ATP-dependent reaction. Changes in intrinsic tryptophan fluorescence upon mixing of enzyme with XMP suggest a conformational change upon substrate binding, likely the ordering of a highly conserved loop in addition to global domain motions. In the GMPS reaction, all forward rates before product release appear to be faster than steady-state turnover, implying that release is likely rate-limiting. These studies establish the functional role of a substrate-induced conformational change in the GMPS catalytic cycle and provide a kinetic context for the formation of an ammonia channel linking the distinct active sites

Automatic measurement of thalamic diameter in 2-D fetal ultrasound brain images using shape prior constrained regularized level sets

© 2013 IEEE. We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: Nonuniform density; missing boundaries; and strong speckle noise. We introduced a 'guitar' structure that represents the negative space surrounding the thalamic regions. The guitar acts as a landmark for deriving the widest points of the thalamus even when its boundaries are not identifiable. We augmented a generalized level-set framework with a shape prior and constraints derived from statistical shape models of the guitars; this framework was used to segment US images and measure the thalamic diameter. Our segmentation method achieved a higher mean Dice similarity coefficient, Hausdorff distance, specificity, and reduced contour leakage when compared to other well-established methods. The automatic thalamic diameter measurement had an interobserver variability of-0.56 2.29 mm compared to manual measurement by an expert sonographer. Our method was capable of automatically estimating the thalamic diameter, with the measurement accuracy on par with clinical assessment. Our method can be used as part of computer-assisted screening tools that automatically measure the biometrics of the fetal thalamus; these biometrics are linked to neurodevelopmental outcomes

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials