How does economic inequality shape conspiracy theories? Empirical evidence from China.

Conspiracy theories tend to be prevalent, particularly in societies with high economic inequality. However, few studies have examined the relationship between economic inequality and belief in conspiracy theories. We propose that economic inequality leads people to believe conspiracy theories about economically advantaged groups (i.e., upwards conspiracy theories) and that moral evaluations of those groups mediate this relationship. Study 1 (N = 300) found support for these ideas in a survey among Chinese residents. Study 2 (N = 160) manipulated participants' perceptions of economic inequality in a virtual society. The manipulation shaped moral evaluations of economically advantaged groups, and conspiracy beliefs, in the predicted manner. In Study 3 (N = 191) and Study 4 (N = 210), we experimentally manipulated participants' perceptions of economic inequality in real Chinese society and replicated the results of Study 2. In addition, in Study 4, we find that economic inequality predicts belief in conspiracy theories about economically disadvantaged groups (i.e., downward conspiracy theories), which was mediated by anomie. We conclude that perceived economic inequality predicts conspiracy theories about economically advantaged groups and that moral evaluations account for this effect. Also, upward and downward conspiracy theory beliefs are associated with different psychological processes.

Flood Footprint Assessment: A Multiregional Case of 2009 Central European Floods.

Hydrometeorological phenomena have increased in intensity and frequency in last decades, with Europe as one of the most affected areas. This accounts for considerable economic losses in the region. Regional adaptation strategies for costs minimization require a comprehensive assessment of the disasters' economic impacts at a multiple-region scale. This article adapts the flood footprint method for multiple-region assessment of total economic impact and applies it to the 2009 Central European Floods event. The flood footprint is an impact accounting framework based on the input-output methodology to economically assess the physical damage (direct) and production shortfalls (indirect) within a region and wider economic networks, caused by a climate disaster. Here, the model is extended through the capital matrix, to enable diverse recovery strategies. According to the results, indirect losses represent a considerable proportion of the total costs of a natural disaster, and most of them occur in nonhighly directly impacted industries. For the 2009 Central European Floods, the indirect losses represent 65% out of total, and 70% of it comes from four industries: business services, manufacture general, construction, and commerce. Additionally, results show that more industrialized economies would suffer more indirect losses than less-industrialized ones, in spite of being less vulnerable to direct shocks. This may link to their specific economic structures of high capital-intensity and strong interindustrial linkages.

Sequence capture using AFLP-generated baits: A cost-effective method for high-throughput phylogenetic and phylogeographic analysis.

Target sequence capture is an efficient technique to enrich specific genomic regions for high-throughput sequencing in ecological and evolutionary studies. In recent years, many sequence capture approaches have been proposed, but most of them rely on commercial synthetic baits which make the experiment expensive. Here, we present a novel sequence capture approach called AFLP-based genome sequence capture (AFLP Capture). This method uses the AFLP (amplified fragment length polymorphism) technique to generate homemade capture baits without the need for prior genome information, thus is applicable to any organisms. In this approach, biotinylated AFLP fragments representing a random fraction of the genome are used as baits to capture the homologous fragments from genomic shotgun sequencing libraries. In a trial study, by using AFLP Capture, we successfully obtained 511 orthologous loci (>700,000 bp in total length) from 11 Odorrana species and more than 100,000 single nucleotide polymorphisms (SNPs) in four analyzed individuals of an Odorrana species. This result shows that our method can be used to address questions of various evolutionary depths (from interspecies level to intraspecies level). We also discuss the flexibility in bait preparation and how the sequencing data are analyzed. In summary, AFLP Capture is a rapid and flexible tool and can significantly reduce the experimental cost for phylogenetic studies that require analyzing genome-scale data (hundreds or thousands of loci).

Quality assessment of clinical practice guidelines for integrative medicine in China: A systematic review.

OBJECTIVE: To assess the quality of integrative medicine clinical practice guidelines (CPGs) published before 2014. METHODS: A systematic search of the scientific literature published before 2014 was conducted to select integrative medicine CPGs. Four major Chinese integrated databases and one guideline database were searched: the Chinese Biomedical Literature Database (CBM), the China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Data, and the China Guideline Clearinghouse (CGC). Four reviewers independently assessed the quality of the included guidelines using the Appraisal of Guidelines for Research and Evaluation (AGREE) II Instrument. Overall consensus among the reviewers was assessed using the intra-class correlation coefficient (ICC). RESULTS: A total of 41 guidelines published from 2003 to 2014 were included. The overall consensus among the reviewers was good [ICC: 0.928; 95% confifi dence interval (CI): 0.920 to 0.935]. The scores on the 6 AGREE domains were: 17% for scope and purpose (range: 6% to 32%), 11% for stakeholder involvement (range: 0 to 24%), 10% for rigor of development (range: 3% to 22%), 39% for clarity and presentation (range: 25% to 64%), 11% for applicability (range: 4% to 24%), and 1% for editorial independence (range: 0 to 15%). CONCLUSIONS: The quality of integrative medicine CPGs was low, the development of integrative medicine CPGs should be guided by systematic methodology. More emphasis should be placed on multi-disciplinary guideline development groups, quality of evidence, management of funding and conflfl icts of interest, and guideline updates in the process of developing integrative medicine CPGs in China.

Measuring and partitioning the high-order linkage disequilibrium by multiple order Markov chains.

A map of the background levels of disequilibrium between nearby markers can be useful for association mapping studies. In order to assess the background levels of linkage disequilibrium (LD), multilocus LD measures are more advantageous than pairwise LD measures because the combined analysis of pairwise LD measures is not adequate to detect simultaneous allele associations among multiple markers. Various multilocus LD measures based on haplotypes have been proposed. However, most of these measures provide a single index of association among multiple markers and does not reveal the complex patterns and different levels of LD structure. In this paper, we employ non-homogeneous, multiple order Markov Chain models as a statistical framework to measure and partition the LD among multiple markers into components due to different orders of marker associations. Using a sliding window of multiple markers on phased haplotype data, we compute corresponding likelihoods for different Markov Chain (MC) orders in each window. The log-likelihood difference between the lowest MC order model (MC0) and the highest MC order model in each window is used as a measure of the total LD or the overall deviation from the gametic equilibrium for the window. Then, we partition the total LD into lower order disequilibria and estimate the effects from two-, three-, and higher order disequilibria. The relationship between different orders of LD and the log-likelihood difference involving two different orders of MC models are explored. By applying our method to the phased haplotype data in the ENCODE regions of the HapMap project, we are able to identify high/low multilocus LD regions. Our results reveal that the most LD in the HapMap data is attributed to the LD between adjacent pairs of markers across the whole region. LD between adjacent pairs of markers appears to be more significant in high multilocus LD regions than in low multilocus LD regions. We also find that as the multilocus total LD increases, the effects of high-order LD tends to get weaker due to the lack of observed multilocus haplotypes. The overall estimates of first, second, third, and fourth order LD across the ENCODE regions are 64, 23, 9, and 3%.

A semiparametric approach for composite functional mapping of dynamic quantitative traits.

Functional mapping has emerged as a powerful tool for mapping quantitative trait loci (QTL) that control developmental patterns of complex dynamic traits. Original functional mapping has been constructed within the context of simple interval mapping, without consideration of separate multiple linked QTL for a dynamic trait. In this article, we present a statistical framework for mapping QTL that affect dynamic traits by capitalizing on the strengths of functional mapping and composite interval mapping. Within this so-called composite functional-mapping framework, functional mapping models the time-dependent genetic effects of a QTL tested within a marker interval using a biologically meaningful parametric function, whereas composite interval mapping models the time-dependent genetic effects of the markers outside the test interval to control the genome background using a flexible nonparametric approach based on Legendre polynomials. Such a semiparametric framework was formulated by a maximum-likelihood model and implemented with the EM algorithm, allowing for the estimation and the test of the mathematical parameters that define the QTL effects and the regression coefficients of the Legendre polynomials that describe the marker effects. Simulation studies were performed to investigate the statistical behavior of composite functional mapping and compare its advantage in separating multiple linked QTL as compared to functional mapping. We used the new mapping approach to analyze a genetic mapping example in rice, leading to the identification of multiple QTL, some of which are linked on the same chromosome, that control the developmental trajectory of leaf age.

[Genetic linkage between Atrn gene and microsatellite markers and the effects to some economic traits in pigs].

The genetic polymorphisms of Atrn gene, which were from 174 F0-F5 Danish resource family pigs (DRF) that has undergone five successive generations of inbreeding and 129 F9 Hunan yellow pigs (HY) that were developed by crossbreeding, were tested by PCR-RFLP method, and the effects to carcass and growth traits were also analyzed. The results showed that: (1) Three genotypes of AA, AB, BB were detected by Taq I-RFLP in the two pig breeds, and highly significant differences (P<0.01) in genotype frequencies were found between the two breeds; (2) Based on the linkage analysis of resource family pigs using CRI-MAP V2.4 software, Atrn gene loci in DRF was found to be linked with SW1031 loci on chromosome 17, the recombination frequency and LOD score for Atrn gene were 0.21 and 3.19 respectively; (3) With the least square analysis of multi-variable model, the significant differences among Atrn genotypes were found in ADSG and ABF traits in DRF and also in ADG, ABF and LMP traits in HY (P<0.05 or P<0.01), BB genotype pigs were superior to AA or AB genotype pigs. Based on the above mentioned results, B gene has favorable effects in important economic traits, and thus selection to BB genotype will get favorable effects in important economic traits. This will provide theory basis for Atrn gene being regarded as a candidate gene in pig carcass and growth traits.

Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species.

With the advent of new molecular marker technologies, it is now feasible to initiate genome projects for outcrossing plant species, which have not received much attention in genetic research, despite their great agricultural and environmental value. Because outcrossing species typically have heterogeneous genomes, data structure for molecular markers representing an entire genome is complex: some markers may have more alleles than others, some markers are codominant whereas others are dominant, and some markers are heterozygous in one parent but fixed in the other parent whereas the opposite can be true for other markers. A major difficulty in analyzing these different types of marker at the same time arises from uncertainty about parental linkage phases over markers. In this paper, we present a general maximum-likelihood-based algorithm for simultaneously estimating linkage and linkage phases for a mixed set of different marker types containing fully informative markers (segregating 1:1:1:1) and partially informative markers (or missing markers, segregating 1:2:1, 3:1, and 1:1) in a full-sib family derived from two outbred parent plants. The characterization of linkage phases is based on the posterior probability distribution of the assignment of alternative alleles at given markers to two homologous chromosomes of each parent, conditional on the observed phenotypes of the markers. Two- and multi-point analyses are performed to estimate the recombination fraction and determine the most likely linkage phase between different types of markers. A numerical example is presented to demonstrate the statistical properties of the model for characterizing the linkage phase between markers.