The Relationship of Acculturation Strategies to Resilience: The Moderating Impact of Social Support among Qiang Ethnicity following the 2008 Chinese Earthquake

<div><p>International research has mostly confirmed the positive association between acculturation strategies and resilience in ethnic groups, but the mediating and moderating mechanisms underlying the relationships are still under-investigated. The present study aimed to investigate the associations between acculturation strategies (based on two cultural identities) and resilience of 898 Qiang ethnicity volunteers (mean age = 29.5), especially exploring the mediating and moderating effects of personality, spiritual belief and social support on the relationship between acculturation strategy (using two cultural identities as latent variables in model analysis) and resilience following the occurrence of 2008 Wenchuan earthquake in Sichuan, taking such mechanisms into account. Results were as follows: (1) All variable presented significant positive correlations; (2) Consistent with the mediating hypotheses, personality and spiritual beliefs played a partial mediating role in the relationship between two cultural identities and resilience; (3) High or low level of perceived social support had a moderating effect on cultural identities and resilience; (4) The integration strategy was the most optimal style to promote the development of resilience, but marginalization was the least effective style.</p></div

The interaction graph of resilience.

<p>Note. Qiang cultural identity = QCI, National cultural identity = NCI, C1 = Low QCI and Low NCI, C2 = High QCI and Low NCI, C3 = Low QCI and High NCI, C4 = High QCI and High NCI.</p

The conceptual model of resilience.

<p>The conceptual model of resilience.</p

The mediation effect structural equation model of fit indices.

<p>The mediation effect structural equation model of fit indices.</p

The moderation effect structural equation model of fit indices.

<p>The moderation effect structural equation model of fit indices.</p

Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results

Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein–ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein–ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of “scoring power” (binding affinity prediction), “ranking power” (relative ranking prediction), “docking power” (binding pose prediction), and “screening power” (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score^HM, ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein–ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein–ligand complexes also need attention

Neonatal MatDup.dist7 mouse rescued by ICR^Δ.

<p>MatDup.dist7 (ICR^Δ.+, <i>Cdkn1c</i>^+.+) albino neonate with two agouti littermates at 10 days post-partum. Mating scheme to breed these animals was as described in the legend to <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000803#pgen-1000803-g002" target="_blank">Figure 2</a>.</p

Observations on AMBER Force Field Performance under the Conditions of Low pH and High Salt Concentrations

Molecular dynamics simulations have become an important tool for the study of structures, dynamics, and functions of biomolecules. Time scales and force field accuracy are key factors for the reliability of these simulations. With the advancement of computational platforms and simulation technologies, all-atom simulations of proteins in explicitly represented aqueous solutions can reach as long as milliseconds. However, there are indications of minor force field flaws in literature. In this work we present our observations on force field accuracy under uncommon conditions. We performed molecular dynamics simulations of BBL refolding in aqueous solutions of acidic pH and high salt concentrations (up to 6 M) with the AMBER99SB-ILDN force field for a microsecond time scale. The reliability of the simulations relies on the accuracy of the physical models of protein, water, and ions. Our simulations show the same trend as experiments: higher salt concentration facilities refolding. However, we have observed the presence of β-sheet in the native helical region as well as α-helix and β-sheet in the native loop region. Some of the nonnative secondary structures are even more stable than native helices. Aside from the secondary structure issue under the uncommon conditions, we have found that the rigidity of glycine dihedral angles in the loop region leads to low root-mean-square deviations but large topological differences from the native structure. Whether this is due to a force field deficiency or not needs further investigations. Recently developed ion parameters exhibit evident liquid features even in the 6 M LiCl solution. However, cation–anion interactions in TIP3P water still seem too strong, leading to high fractions of contact ion pairs. We do not find any specific ion-binding motif, thus we conclude that the effect of salt is a nonspecific electrostatic screening in our simulations. Our observations on the AMBER force field performance under acidic conditions and high salt concentrations show that simulations under extreme conditions can provide informative tests of physical models

Zero-order correlations, descriptive statistics among all variables (<i>N</i> = 898).

<p>Zero-order correlations, descriptive statistics among all variables (<i>N</i> = 898).</p

Weight gain in post-partum MatDup.dist7 mice rescued by ICR^Δ.

<p>(A) Weight gain in litter matched MatDup.dist7 (ICR^Δ.+, <i>Cdkn1c</i>^+.+) or (ICR^Δ.Δ, <i>Cdkn1c</i>^+.+) young obtained from matings of (T9H/+, <i>Tyr^c</i>^/<i>c</i>, ICR^Δ/+ ♀×T9H/+, Tyr^+/+, ICR^+/+ ♂) and (T9H/+, <i>Tyr^c</i>^/<i>c</i>, ICR^Δ/+, <i>Cdkn1c</i>^+/− ♀×T9H/+, <i>Tyr</i>^+/+, ICR^+/+, <i>Cdkn1c</i>^+/+ ♂) mice. Each graph represents a single litter where at least one control with the same number of active <i>Igf2</i> alleles was obtained. Each bar represents a single animal. The paired-sample <i>t</i>-test was used to determine the probability that the weight of MatDup.dist7 and control mice was equal. For three-animal litters, the pairs used in the statistical test are indicated by the bracket above the bars. The <i>P</i> value was <0.01 for 6 week old mice, indicating a significant difference, while there was no significant difference in weight at 1, 2, and 3 weeks of age. (B) All weight gain data collected from the two sets of matings as described in (A) regardless of littermate matching. Bars are mean±s.d., with (n) given above the 1 wk bars. The two-sample <i>t</i>-test was used to determine the probability that two 6 week samples (identified as paired squares) were equal. Key: alleles are maternal.maternal in derivation for MatDup.dist7 and maternal/paternal in derivation for controls.</p