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The high reproducibility of the crystallization process in combination with the high sequence identity between TgAldolase and PfAldolase will hopefully make this system a viable tool for studying the interactions that are important in Plasmodium.

We would like to thank the Stanford Synchrotron Radiation Lightsource beamline support staff and to acknowledge the Seattle Structural Genomics Center for Infectious Disease for providing the expression clone used in this work. National Center for Biotechnology Information , U. Published online Aug Author information Article notes Copyright and License information Disclaimer.

Received Jun 25; Accepted Jul This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. This article has been cited by other articles in PMC. Abstract The apicomplexan parasite Toxoplasma gondii must invade host cells to continue its lifecycle. Table 1 Macromolecule-production information. Open in a separate window.

This construct uses the second start codon in the expected mRNA transcript, which produces a product homologous to other fructose-1,6-bisphosphate aldolases. This construct is 71 amino acids shorter than the gene product listed in the database. The sequence is amino acids in total. This construct lacks seven amino acids at the N-terminus and 23 amino acids at the C-terminus compared with the sequence deposited in ToxoDB.

Table 3 Data collection and processing Values in parentheses are for the outer shell. Table 4 Refinement statistics Values in parentheses are for the outer shell. Supplementary Material PDB reference: D 66 , — USA , , — D 66 , 12— D 62 , — D 64 , 83— The Proteomics Protocols Handbook , edited by J. Cell , 11 , — Biochemistry , 37 , — PLoS One , 5 , e D 67 , — BMC Bioinformatics , 9 , Articles from Acta Crystallographica. Support Center Support Center. Please review our privacy policy.

Vapor diffusion, sitting drop, seeded. Protein concentration mgml 1. Format see all Format. All Listings filter applied. Brand see all Brand. Brand Type see all Brand Type. Condition see all Condition. New with tags 2. Item Location see all Item Location. At decays as a power law function with the scaling exponent equal to 0. At for C implies very weak or zero cross-correlations among world-index returns for larger At, which agrees with the empirical finding that world indices are often uncorrelated in returns.

Our findings of long-range cross-correlations in magnitudes among the world indices is, besides a finding in Ref. World market risk decays very slowly. Once the volatility risk is transmitted across the world, the risk lasts a long time.

In GFM, the time lag covariance between each pair of indices is proportional to the autocovariance of the global factor. For example, if there is short-range autocovariance for Mt and long-range autocovariance for Mf, then for individual indices the cross- covariance between returns will be short-range and the cross-covariance between magnitudes will be long-range.

Therefore, the properties of time-lag cross-correlation in multiple time series can be modeled with a single time series — the global factor Mt. The relationship between time lag covariance among two index returns and autocovariance of the global factor also holds for the relationship between time lag cross-correlations among two index returns and auto-correlation function of the global factor, because it only need to normalize the original time series to mean zero and standard deviation one.

Estimation of the Global factor In contrast to domestic markets, where for a given country we can choose the stock index as an estimator of the "global" factor, when we study world markets the global factor is unobservable. At the world level when we study cross-correlations among world markets, we estimate the global factor using principal component analysis PCA 27 1.

In this section we use bold font for N dimensional vectors or NxN matrix, and underscore t for time series. We express zt as the sum of the first part of Eq. In the rest of this work, we apply the method of Eq. Analysis of the global factor Next we apply the method of Eq.

Precisely, for the world indices. For the 48 world index returns. The auto-correlation properties of the global factor are the same as the auto-correlation properties of the indi- vidual indices, i.

These results are also in agreement with Fig. At calculated for C decays more slowly than the largest singular value calculated for C. As found in Ref. Therefore, for simplicity, we only consider magnitude cross-correlations in modeling the global factor. We expect the parameter 7 to be positive, implying that "bad news" negative increments increases volatihty more than "good news". Next we test the hypothesis that a significant percentage of the world cross-correlations can be explained by the global factor.

By using PGA we find that the global factor can account for Note that, according to RMT, only the eigenval- ues larger than the largest eigenvalue of a Wishart matrix calculated by Eq. To calculate the percentage of variance the significant cx. From all the 48 eigenvalues, only the ffist three are significant: PGA is defined to estimate the percentage of variance the global factor can account for zero time lag correlations. Next we study the time lag cross-correlations after removing the global trend, and apply the SVD to the correlation matrix of regression residuals rji of each index [see Eq.

Our results show that for both returns and magnitudes, the remaining cross- correlations are very small for all time lags compared to cross-correlations obtained for the original time series.

This result additionally conffims that a large fraction of the world cross- correlations for both returns and magnitudes can be explained by the global factor. For the market factor the conditional volatility at can be estimated by recursion using the historical conditional volatilities and fitted coefficients in Eq. For example, the largest cluster at the end of the graph shows the financial crisis.

The clusters in the conditional volatilities may serve to predict market crashes. In each cluster, the height is a measure of the size of the market crash, and the width indicates its duration. Finding uncorrelated individual indices Next, in Fig. There are indices for which cross-correlations with the global factor are very small compared to the other indices; 10 of 48 indices have cross- correlations coeffi- cients with the global factor smaller than 0.

The financial mar- ket of each of these countries is weakly bond with financial markets of other countries. This is useful information for investment managers because one can reduce the risk by investing in these countries during world market crashes which, seems, do not severely infiuence these countries. Applying the mod- ified TLRMT to the daily data for 48 world-wide financial indices, we find short-range cross-correlations between the returns, and long-range cross-correlations between their mag- nitudes.

The magnitude cross-correlations show a power law decay with time lag, and the 15 scaling exponent is 0. The result we obtain, that at the world level the cross-correlations between the magnitudes are long-range, is potentially significant because it implies that strong market crashes introduced at one place have an extended duration elsewhere — which is "bad news" for international investment managers who imagine that diversification across countries reduces risk.

We model long-range world-index cross-correlations by introducing a global factor model in which the time lag cross-correlations between returns magnitudes can be explained by the auto-correlations of the returns magnitudes of the global factor. We estimate the global factor as the first component by using principal component analysis. Using random matrix theory, we find that only three principal components arc significant in explaining the cross-correlations.

The global factor accounts for Therefore, in most cases, a single global factor is sufficient. We also show the applications of the GFM, including locating and forecasting world risk, and finding individual indices that are weakly correlated to the world economy. Locating and forecasting world risk can be realized by fitting the global factor using a GJR-GARCH 1,1 model, which explains both the volatility correlations and the asymmetry in the volatility response to both "good news" and "bad news.

To find the indices that are weakly correlated to the world economy, we calculate the correlation between the global factor and each individual index. We find 10 indices which have a correlation smaller than 0. To reduce risk, investment managers can increase the proportion of investment in these countries during world market crashes, which do not severely infiuence these countries.

Based on principal component analysis, we propose a general method which helps extract the most significant components in explaining long-range cross-correlations.

This makes the method suitable for broad range of phenomena where time series are measured, ranging from seismology and physiology to atmospheric geophysics. We expect that the cross-correlations in EEG signals are dominated by the small number of most significant components control- hng the cross-correlations.

We speculate that cross-correlations in earthquake data are also 16 controlled by some major components. Thus the method may have significant predictive and diagnostic power that could prove useful in a wide range of scientific fields. We thank Ivo Grosse and T. Preis for valuable discussions and NSF for financial support. Paul, Science , Journal 52, Analysts Journal 50,

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The high reproducibility of the crystallization process in combination with the high sequence identity between TgAldolase and PfAldolase will hopefully make this system a viable tool for studying the interactions that are important in Plasmodium. Items in search results.

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