Preparation of pure lithium hexafluoroarsenate Final report

Preparation and analysis of high purity lithium hexafluoroarsenat

First-principles investigation of 180-degree domain walls in BaTiO_3

We present a first-principles study of 180-degree ferroelectric domain walls in tetragonal barium titanate. The theory is based on an effective Hamiltonian that has previously been determined from first-principles ultrasoft-pseudopotential calculations. Statistical properties are investigated using Monte Carlo simulations. We compute the domain-wall energy, free energy, and thickness, analyze the behavior of the ferroelectric order parameter in the interior of the domain wall, and study its spatial fluctuations. An abrupt reversal of the polarization is found, unlike the gradual rotation typical of the ferromagnetic case.Comment: Revtex (preprint style, 13 pages) + 3 postscript figures. A version in two-column article style with embedded figures is available at http://electron.rutgers.edu/~dhv/preprints/index.html#pad_wal

Nonlinear conditional model bias estimation for data assimilation

In this study, we develop model bias estimators based on an asymptotic expansion of the model dynamics for small time scales and small perturbations in a model parameter, and then use the estimators to improve the performance of a data assimilation system. We employ the well-known Lorenz (1963) model so that we can study all aspects of the dynamical system and model bias estimators in a detailed way that would not be possible with a full physics numerical weather prediction model. In particular, we first work out the asymptotics of the Lorenz model for small changes in one of its parameters and then use statistics from cycled data assimilation experiments to demonstrate that the asymptotics accurately represent the behavior of the model and that the coefficients of the nonlinear asymptotical expansion can be reasonably estimated by solving a least squares minimization problem. In data assimilation, the background error covariance matrix usually estimates the uncertainty of the model background, which is then used along with the observation error covariance matrix to produce an updated analysis. If the uncertainty of the model background is strongly influenced by time-dependent model biases, then the development of nonlinear bias estimators that also vary with time could improve the performance of the assimilation system and the accuracy of the updated analysis. We demonstrate this improvement through the combination of a constant background error covariance matrix with a dynamically-varying matrix computed using the model bias estimators. Numerical tests using the Lorenz (1963) model illustrate the feasibility of the approach and show that it leads to clear improvements in the analysis and forecast accuracy

Identification of Commercially Available Antibodies that Block Ligand Binding by BMPR2

Osteoporosis, a disease of low bone mineral density, affects 10 million Americans and triggers significant health problems and considerable socioeconomic burdens. Current treatments for osteoporosis have significant limitations, necessitating identifying new treatment strategies via building a better understanding of the endogenous mechanisms regulating bone mass. A recent study demonstrated that removal of the BMP type 2 receptor (BMPR2) in skeletal progenitor cells of Bmpr2-cKO mice during embryonic development leads to reduced age-related bone loss by sustained elevation in bone formation rate. This present study sought to advance the translational potential of the genetic model by identifying antibodies that neutralize the ligand-binding function of the BMPR2 extracellular domain (BMPR2-ECD). This study first established a modified, cell-free immunoprecipitation assay wherein the ligand BMP2 was pulled-down by BMPR2-ECD conjugated to Protein G beads; the unbound BMP2 (found in the supernatant) was subsequently quantified by ELISA. This yielded a standard assay wherein approximately 2 ug BMPR2-ECD leads to a 70% reduction in BMP2 signal. Next, the neutralizing ability of 3F6, a mouse monoclonal antibody raised against the ligand-binding region of BMPR2, was examined and was found to cause a dose-dependent inhibition of BMPR2-ECD ligand-binding. Given the ascites preparation of 3F6, specificity of this assay was confirmed by demonstrating that ligand-binding activity of BMPR2-ECD is unchanged in the presence of non-specific, negative-control ascites. Using these results as a guide, 1F12, another mouse monoclonal antibody raised against the ligand-binding region of BMPR2, was evaluated and was also found to neutralize the ligand-binding function of BMPR2-ECD. In contrast, no effect on ligand-binding function of BMPR2-ECD was observed with 9A10 even though this mouse monoclonal antibody is also raised against the ligand-binding region of BMPR2. These results provide proof-of-concept data for future studies evaluating inhibition of BMPR2 function in vivo as a means to reduce age-related bone loss

Thermal Diffusion and Quench Propagation in YBCO Pancake Coils Wound with ZnO-and Mylar Insulations

The thermal diffusion properties of several different kinds of YBCO insulations and the quench properties of pancake coils made using these insulations were studied. Insulations investigated include Nomex, Kapton, and Mylar, as well as insulations based on ZnO, Zn2GeO4, and ZnO-Cu. Initially, short stacks of YBCO conductors with interlayer insulation, epoxy, and a central heater strip were made and later measured for thermal conductivity in liquid nitrogen. Subsequently, three different pancake coils were made. The first two were smaller, each using one meter total of YBCO tape present as four turns around a G-10 former. One of these smaller coils used Mylar insulation co-wound with the YBCO tape, the other used YBCO tape onto which ZnO based insulation had been deposited. One larger coil was made which used 12 total meters of ZnO-insulated tape and had 45 turns. The results for all short sample and coil thermal conductivities were ~1-3 Wm-1K-1. Finally, quench propagation velocity measurements were performed on the coils (77 K, self field) by applying a DC current and then using a heater pulse to initiate a quench. Normal zone propagation velocity (NZP) values were obtained for the coils both in the radial direction and in the azimuthal direction. Radial NZP values (0.05-0.7 mm/s) were two orders of magnitude lower than axial values (~14-17 mm/s). Nevertheless, the quenches were generally seen to propagate radially within the coils, in the sense that any given layer in the coil is driven normal by the layer underneath it.Comment: 58 pages, 5 tables, 16 fig

The effect of zinc on human taste perception

Zinc salts are added as a nutritional or functional ingredient in food and oral care products. The 1st experiment in this study investigated the taste and somatosensory effect of zinc salts (chloride, iodide, sulfate, bromide, acetate). The zinc salts had very little taste (bitter, salty, savory, sour, sweet), and the taste that was present was easily washed away with water rinses. The major oral quality of zinc was astringency, and the astringency lingered beyond expectoration. The 2nd experiment combined zinc salts with prototypical stimuli eliciting basic tastes. Zinc was a potent inhibitor of sweetness and bitterness (&gt;70% reduction in taste) but did not affect salt, savory, or sour taste.<br /

Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring

Defining intracellular protein concentration is critical in molecular systems biology. Although strategies for determining relative protein changes are available, defining robust absolute values in copies per cell has proven significantly more challenging. Here we present a reference data set quantifying over 1800 Saccharomyces cerevisiae proteins by direct means using protein-specific stable-isotope labeled internal standards and selected reaction monitoring (SRM) mass spectrometry, far exceeding any previous study. This was achieved by careful design of over 100 QconCAT recombinant proteins as standards, defining 1167 proteins in terms of copies per cell and upper limits on a further 668, with robust CVs routinely less than 20%. The selected reaction monitoring-derived proteome is compared with existing quantitative data sets, highlighting the disparities between methodologies. Coupled with a quantification of the transcriptome by RNA-seq taken from the same cells, these data support revised estimates of several fundamental molecular parameters: a total protein count of ∼100 million molecules-per-cell, a median of ∼1000 proteins-per-transcript, and a linear model of protein translation explaining 70% of the variance in translation rate. This work contributes a “gold-standard” reference yeast proteome (including 532 values based on high quality, dual peptide quantification) that can be widely used in systems models and for other comparative studies. Reliable and accurate quantification of the proteins present in a cell or tissue remains a major challenge for post-genome scientists. Proteins are the primary functional molecules in biological systems and knowledge of their abundance and dynamics is an important prerequisite to a complete understanding of natural physiological processes, or dysfunction in disease. Accordingly, much effort has been spent in the development of reliable, accurate and sensitive techniques to quantify the cellular proteome, the complement of proteins expressed at a given time under defined conditions (1). Moreover, the ability to model a biological system and thus characterize it in kinetic terms, requires that protein concentrations be defined in absolute numbers (2, 3). Given the high demand for accurate quantitative proteome data sets, there has been a continual drive to develop methodology to accomplish this, typically using mass spectrometry (MS) as the analytical platform. Many recent studies have highlighted the capabilities of MS to provide good coverage of the proteome at high sensitivity often using yeast as a demonstrator system (4⇓⇓⇓⇓⇓–10), suggesting that quantitative proteomics has now “come of age” (1). However, given that MS is not inherently quantitative, most of the approaches produce relative quantitation and do not typically measure the absolute concentrations of individual molecular species by direct means. For the yeast proteome, epitope tagging studies using green fluorescent protein or tandem affinity purification tags provides an alternative to MS. Here, collections of modified strains are generated that incorporate a detectable, and therefore quantifiable, tag that supports immunoblotting or fluorescence techniques (11, 12). However, such strategies for copies per cell (cpc) quantification rely on genetic manipulation of the host organism and hence do not quantify endogenous, unmodified protein. Similarly, the tagging can alter protein levels - in some instances hindering protein expression completely (11). Even so, epitope tagging methods have been of value to the community, yielding high coverage quantitative data sets for the majority of the yeast proteome (11, 12). MS-based methods do not rely on such nonendogenous labels, and can reach genome-wide levels of coverage. Accurate estimation of absolute concentrations i.e. protein copy number per cell, also usually necessitates the use of (one or more) external or internal standards from which to derive absolute abundance (4). Examples include a comprehensive quantification of the Leptospira interrogans proteome that used a 19 protein subset quantified using selected reaction monitoring (SRM)1 to calibrate their label-free data (8, 13). It is worth noting that epitope tagging methods, although also absolute, rely on a very limited set of standards for the quantitative western blots and necessitate incorporation of a suitable immunogenic tag (11). Other recent, innovative approaches exploiting total ion signal and internal scaling to estimate protein cellular abundance (10, 14), avoid the use of internal standards, though they do rely on targeted proteomic data to validate their approach. The use of targeted SRM strategies to derive proteomic calibration standards highlights its advantages in comparison to label-free in terms of accuracy, precision, dynamic range and limit of detection and has gained currency for its reliability and sensitivity (3, 15⇓–17). Indeed, SRM is often referred to as the “gold standard proteomic quantification method,” being particularly well-suited when the proteins to be quantified are known, when appropriate surrogate peptides for protein quantification can be selected a priori, and matched with stable isotope-labeled (SIL) standards (18⇓–20). In combination with SIL peptide standards that can be generated through a variety of means (3, 15), SRM can be used to quantify low copy number proteins, reaching down to ∼50 cpc in yeast (5). However, although SRM methodology has been used extensively for S. cerevisiae protein quantification by us and others (19, 21, 22), it has not been used for large protein cohorts because of the requirement to generate the large numbers of attendant SIL peptide standards; the largest published data set is only for a few tens of proteins. It remains a challenge therefore to robustly quantify an entire eukaryotic proteome in absolute terms by direct means using targeted MS and this is the focus of our present study, the Census Of the Proteome of Yeast (CoPY). We present here direct and absolute quantification of nearly 2000 endogenous proteins from S. cerevisiae grown in steady state in a chemostat culture, using the SRM-based QconCAT approach. Although arguably not quantification of the entire proteome, this represents an accurate and rigorous collection of direct yeast protein quantifications, providing a gold-standard data set of endogenous protein levels for future reference and comparative studies. The highly reproducible SIL-SRM MS data, with robust CVs typically less than 20%, is compared with other extant data sets that were obtained via alternative analytical strategies. We also report a matched high quality transcriptome from the same cells using RNA-seq, which supports additional calculations including a refined estimate of the total protein content in yeast cells, and a simple linear model of translation explaining 70% of the variance between RNA and protein levels in yeast chemostat cultures. These analyses confirm the validity of our data and approach, which we believe represents a state-of-the-art absolute quantification compendium of a significant proportion of a model eukaryotic proteome