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A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases

A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases

MOD score is dependent on the size and structure of the pedigrees under study [10]. However, the influence of pedigree structure on the power to detect linkage with the MOD score has not been investigated so far. It is well known that large, multigeneration pedigrees are the most informative for linkage analysis. However, it is not always possible to recruit such families. Given that two- generation families may be the most feasible to study, it is essential to investigate how the sample structure may im- prove the detection of linkage when complex diseases are present. The present work focuses on the power of para- metric and nonparametric linkage statistics to detect the effect of genes for complex diseases using different pedi- gree structures. We first conducted simulations under the null hypothesis of no linkage to see the influence of pedi- gree structure on the distribution of the parametric scores (LOD and MOD) and on the nonparametric scores (NPL and KC-LOD). Second, we examine the power of these test statistics to detect linkage for each pedigree structure and discuss which is the best test statistic given the sample under study.

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A novel approach to simulate gene environment interactions in complex diseases

A novel approach to simulate gene environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones.

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The cultural epigenetics of psychopathology: The missing heritability of complex diseases found?

The cultural epigenetics of psychopathology: The missing heritability of complex diseases found?

More generally, simple arguments from genetic determinism regarding men- tal disorders fail, in part because of a draconian population bottleneck that, early in our species’ history, resulted in an overall genetic diversity less than that observed within and between contemporary chimpanzee subgroups. Mano- lio et al. (2009) describe this conundrum more generally in terms of ‘finding the missing heritability of complex diseases’. They observe, for example, that at least 40 loci have been associated with human height, a classic complex trait with an estimated heritability of about 80 %, yet they explain only about 5 % of phenotype variance despite studies of tens of thousands of people. This result, they find, is typical across a broad range of supposedly heritable diseases, and call for extending beyond current genome-wide assoication approaches to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

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Prioritization and functional assessment of noncoding variants associated with complex diseases

Prioritization and functional assessment of noncoding variants associated with complex diseases

example, CADD constructed a model based on the training variants that have been under long-term selective pressure, which made it perform less well on certain disease-associated variants under weak evolu- tionary constraint, such as those influencing the risk of complex traits [11, 12]. LINSIGHT [19] was constructed based on the premise of inferring the selective pressure on noncoding sites and worked very well on identifying human noncoding variants associ- ated with inherited diseases; however, this premise may not hold in all cases, such as those in which the variants increase the risk for post-reproductive diseases [19]. In addition, except for genomic annotations and conservation measures, all the currently available methods seldom consider population-level statistical measures (e.g., F statistics [20]), which may be helpful to prioritize common variants. Although supervised learning demands a representative and correctly labeled training set, a major problem for these methods is the use of mislabeled vari- ants in the training stage, which may lead to false predic- tions by supervised classifiers. For example, DIVAN labeled variants from the 1000 Genomes Project as benign with few controlling or filtration steps. A considerable fraction of the variants in the 1000 Genomes is reported to be involved in various complex diseases or traits [21, 22]. CADD labeled fixed or nearly fixed derived alleles in humans as benign and simulated de novo variants as deleterious. However, such simulated de novo variants may contain a substantial proportion of benign variants, which thus may lead to false predictions.

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Sibship T2association tests of complex diseases for tightly linked markers

Sibship T2association tests of complex diseases for tightly linked markers

For population case-control association studies, the false-positive rates can be high due to inappropriate controls, which can occur if there is population admixture or stratification. Moreover, it is not always clear how to choose appropriate controls. Alternatively, the parents or normal sibs can be used as controls of affected sibs. For late-onset complex diseases, parental data are not usually available. One way to study late-onset disorders is to perform sib-pair or sibship analyses. This paper proposes sibship-based Hotelling’s T 2 test statistics for high- resolution linkage disequilibrium mapping of complex diseases. For a sample of sibships, suppose that each sibship consists of at least one affected sib and at least one normal sib. Assume that genotype data of multiple tightly linked markers/haplotypes are available for each individual in the sample. Paired Hotelling’s T 2 test statistics are proposed for high-resolution association studies using normal sibs as controls for affected sibs, based on two coding methods: ‘haplotype/allele coding’ and ‘genotype coding’. The paired Hotelling’s T 2 tests take into account not only the correlation among the markers, but also take the correlation within each sib-pair. The validity of the proposed method is justified by rigorous mathematical and statistical proofs under the large sample theory. The non-centrality parameter approxi- mations of the test statistics are calculated for power and sample size calculations. By carrying out power and simulation studies, it was found that the non-centrality parameter approximations of the test statistics were accurate. By power and type I error analysis, the test statistics based on the ‘haplotype/allele coding’ method were found to be advantageous in comparison to the test statistics based on the ‘genotype coding’ method. The test statistics based on multiple markers can have higher power than those based on a single marker. The test statistics can be applied not only for bi-allelic markers, but also for multi-allelic markers. In addition, the test statistics can be applied to analyse the genetic data of multiple markers which contain double heterozygotes — that is, unknown linkage phase data. An SAS macro, Hotel_sibs.sas, is written to implement the method for data analysis.

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Statistical Methods in Mapping Complex Diseases

Statistical Methods in Mapping Complex Diseases

Genome-wide association studies (GWAS) offer a powerful tool to identify genes that confer moderate disease risks. In these studies, the main outcome of interest is often disease status. However, in many of these studies, a set of correlated sec- ondary phenotypes that may share the same genetic factors with disease status are also collected. Examination of these secondary phenotypes may provide important clues about the disease etiology and supplement the main studies. Various secondary phenotypes have been suggested as useful for gene mapping of complex diseases. For example, low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels for coronary artery disease (Grundy et al. (2004)), and angiotensin-converting enzyme activity for hypertension (Kammerer et al. (2004)) are clear examples of useful secondary phenotypes. In some situations, the analysis of secondary phenotypes may become the primary focus of subsequent studies. Re- cently, there have been several GWAS on secondary phenotypes, such as BMI and lipid levels (Kathiresan et al. (2008); Loos et al. (2008); Teslovich et al. (2010); Willer et al. (2008)), where most of the data came from case-control studies of complex dis- eases, such as diabetes, hypertension, and heart disease.

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Dynamic Bayesian Testing of Sets of Variants in Complex Diseases

Dynamic Bayesian Testing of Sets of Variants in Complex Diseases

associations or cumulative effects of multiple variants are detect- able with improved power by dynamic grouping of sets of variants; and our joint model accounts for correlation among variants, such that multiple disease variants within a local region can be detected and redundant associations due to LD are filtered out. As a consequence, we are able to define sets of variants that overlap with each other without concerning about multicolinearity among variants. A variant may be simultaneously present in the data set as a single variant by itself and as groups of variants with others. The new method will then evaluate the effects of the variant both as a single variant and as groups, and one shows that the most power will be automatically detected. This feature significantly alleviated the burden on the users to define sets of variants to be tested, which is often arbitrary. At the same time, the users can still design their favorable sets of variants for joint testing based on their biological knowledge. While it is unclear how much effects of rare variants contribute to the complex diseases, it is most likely that both common and rare variants are contrib- uting to the disease risks to a different degree. We therefore believe that our method is more suitable to the current genome- wide association studies, where all genetic variants from sequencing studies are included in the analysis.

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A comparison of genomic profiles of complex diseases under different models

A comparison of genomic profiles of complex diseases under different models

We have conducted extensive research to explore algo- rithms under very different approaches to model indi- vidual risk to 7 complex diseases from the WTCCC from genome-wide data. Our purpose was to understand whether current tools may be able to build predictive models which are accurate enough for application in med- ical care. In light of our results, it seems that for only two diseases with a high genetic component (rheumatoid arthritis and Type 1 diabetes) did certain models achieve a high enough predictive capacity for them to be used in clinical practice. The best of these were obtained for these two diseases by a boosting approach which is robust to redundant and noisy variables. Given the good per- formance of the boosting approach and the fact that we only considered one boosting algorithm (AdaboostM1), we believe that more systematic research of the boosting approach for building genome-wide genetic models could provide interesting insights.

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Searching for genes in complex diseases: lessons from systemic lupus erythematosus

Searching for genes in complex diseases: lessons from systemic lupus erythematosus

Although positional cloning has been highly successful in identifying the loci underlying Mendelian diseases, it has been much less so for identifying genes for common, complex disorders. The reason is that Mendelian disorders are genetically simple: they feature a strong correspondence between the presence of a predisposing genotype at a single genetic locus and the pheno- typic outcome. This correspondence produces a strong linkage signal in families and allows for localizing a dis- ease gene by recombination events. The more common familial but com- plex disorders involve numerous loci, which may interact with each other to predispose to disease. Because the total genetic effect is partitioned among several or many loci, the corre- spondence between a predisposing genotype at one such locus and the disease outcome is weaker, greatly reducing the power of linkage analysis. The power of linkage analysis to locate susceptibility loci for complex diseases is much greater in animal models than in humans, for a variety of reasons. First, inbred strains are used, which tends to reduce the genetic com- plexity and limit genetic effects to the loci that differentiate the original

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Knowledge and attitudes to personal genomics testing for complex diseases among Nigerians

Knowledge and attitudes to personal genomics testing for complex diseases among Nigerians

Advances in medical technologies have made genomics tests that predict risk of diseases increasingly available for use in clinical settings. With the substantial reduc- tion in cost and its rapid spread around the world, it is inevitable that genomics tests for complex diseases will soon be available everywhere including Africa. As the number of these tests increases, the uses and interpret- ation of the information they generate will require increased understanding of genomics and how its princi- ples apply to different health problems. Such uses are raising concerns about the ethical issues that may arise when these technologies are used to identify genetic

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<p>Potential Impact of the Multi-Target Drug Approach in the Treatment of Some Complex Diseases</p>

<p>Potential Impact of the Multi-Target Drug Approach in the Treatment of Some Complex Diseases</p>

process. However, the recent Food and Drug Administration (US) approval of pretomanid represents the third anti-TB drug in 50 years. The triple combination of bedaquiline, pretomanid and linezolid has been reported as a potential “game-changer” with high efficacy towards extensively drug-resistant TB. 61 This multi-drug target regimen has been reported to inhibit mycobacterial ATP synthase, myco- lic acid synthesis and energy production. 62 In 2015, the discovery of a potent anti-TB drug, TB47, a pyrazolo[1,5- a]pyridine-3-carboxamide, sparked new hope in the fight against TB. 63 The compound, TB47, was shown to be effec- tive against 37 MDR-TB clinical strains. 64 Furthermore, this compound was shown to be highly antimycobacterial, inhi- biting the growth of M. bovis, M. ulcerans, M. marinum, M. smegmatis and M. abscessus at very low concentrations, ≥0.008 µg/mL of TB47 displayed potent bactericidal activity in comparison to rifampicin (0.2 µg/mL). 63,64 Liu and co- workers (2019) recently demonstrated that TB47 directly interacts with the respiratory cytochrome bcc Complex and is a potential antitubercular agent that synergistically inhibits M. tuberculosis growth in the presence of other first-line drugs. TB47 also inhibited the growth of yeast infection (Candida albicans) and some of ESKAPE superbugs (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeru- ginosa and Escherichia coli). 64,65 The inclusion of TB47 or its derivatives to the current drug regimen may have desirable outcomes in the pursuit of multi-target approaches of various complex diseases. 65,66

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Gene Discovery in Mendelian and Complex Diseases

Gene Discovery in Mendelian and Complex Diseases

Rouault, 2006). Another study in yeast showed NFS1 is also localized to the nucleus by detecting NFS1 subpopulations first targeted to the mitochondria, then transferred to the cytosol and finally targeted to the nucleus (Naamati et al., 2009). The interaction network of proteins involved in Fe-S cluster assembly is becoming clearer in both yeast and mammalian cells. In particular, NFS1, ISCU, ISD11, and FXN form the Fe-S cluster core complex (Bridwell-Rabb et al., 2011; Prischi et al., 2010; Rouault, 2012; Schmucker et al., 2011; Tsai and Barondeau, 2010). Fe-S cluster formation begins with NFS1 forming a homodimer to which monomers of a scaffold protein ISCU, bind near the top and the bottom (Shi et al., 2010). A co-factor, pyridoxal 5’ phosphate, helps NFS1 provide inorganic sulfur from cysteine residues, which then bind to cysteine ligands supplied by ISCU, that further covalently bind to iron (Bandyopadhyay et al., 2008; Raulfs et al., 2008). The core complex then recruits NFS1 binding protein, ISD11 (Adam et al., 2006; Wiedemann et al., 2006) and finally FXN (Prischi et al., 2010; Tsai and Barondeau, 2010). Next, the Fe-S cluster is transferred to recipient apo-proteins via binding of ISCU to chaperone proteins (Craig and Marszalek, 2002; Rouault, 2012). In short, it is clear that Fe-S cluster assembly is a highly conserved multistep process requiring cysteine desulfurases, scaffold proteins, chaperones and iron donors to ultimately maintain iron homeostasis, execute catalysis and gene regulation (Lill et al., 2012; Rouault, 2012).

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Environment-Sensitive Epigenetics and the Heritability of Complex Diseases

Environment-Sensitive Epigenetics and the Heritability of Complex Diseases

Diseases and other phenotypes may also exhibit complex inheritance when epigenetic states are environment sensi- tive. In mice, a mother’s grooming and licking of an off- spring can induce epigenetic changes in the offspring, causing a modified stress response when the offspring reach adulthood (Weaver et al. 2004; Meaney and Szyf 2005; Weaver et al. 2006). The mechanisms governing this system have been reviewed by Weaver (2007). Maternal diet in mice can affect offspring phenotype by increasing methyla- tion rates (Wolff et al. 1998; Cooney et al. 2002; Waterland and Jirtle 2003, 2004; Cropley et al. 2006; Waterland et al. 2006; Lillycrop et al. 2007) or modifying histones (Lillycrop et al. 2007; Sandovici et al. 2011). Silencing the expression of a DNA methyltransferase, Dnmt3, in honeybees induces developmental changes similar to those induced by feeding larvae a diet of royal jelly, suggesting that the diet of honey- bees controls rates of epigenetic modification, which ulti- mately regulates larval development (Kucharski et al. 2008; Elango et al. 2009); the epigenetic modifications are associated with patterns of alternative splicing (Lyko et al. 2010). Recently, evidence for environment-sensitive rates of methylation has been found in humans (Heijmans et al. 2008; Katari et al. 2009; Waterland et al. 2010). Other examples of environmental effects on epigenetic state are reviewed by Jirtle and Skinner (2007).

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Association study design of complex diseases: idiopathic scoliosis

Association study design of complex diseases: idiopathic scoliosis

The aim of a genome wide association study (GWAS) is to detect significant associations in a population between common diseases and common genetic variants. In particular, a GWAS is designed to examine millions of SNPs in the genome, using commercially available chips to survey the genotypes of thousands of individuals. Variant SNP alleles that are differentially associated with a disease cohort compared to controls are thought to denote susceptibility regions that contain genetic correlates to the disease (i.e. genes or genetic deletions/duplications). Any positive association should be confirmed in a different population using a larger sample size (47).

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Measuring and using admixture to study the genetics of complex diseases

Measuring and using admixture to study the genetics of complex diseases

Many US residents can trace their genetic ancestry to more than one continent. The European colonial period that started in the late 1400s brought together in the New World populations that had been geographically isolated, namely, Europeans, West Africans and Native Americans. Given the recent and common origin of all human populations, this admixture had only a small average effect on the gene pools of these new populations. In other words, for most genomic regions, the pre-colonial (or parental) populations had similar allele frequencies and, at these, admixture was of little conse- quence. At some other loci, however, there had been some change in allele frequency in the time since the separation of parental populations and it is at these loci where admixture has had an important effect. Since populations like African Americans, African Caribbeans and Mexican Americans were formed in the recent past, allelic associations in these popu- lations that were created by admixture extend over large distances. Admixed populations represent a useful resource for mapping complex-disease genes by using this long-range ALD, 12 which requires fewer markers to screen the genome than other populations or approaches. Understanding the genetic consequences of admixture is important because it can be both a confounding factor and a source of statistical power in gene identification studies.

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Sequencing Complex Diseases With HapMap

Sequencing Complex Diseases With HapMap

Table 1 lists all possible genotypes and diplotypes Suppose there is a random sample of size n drawn at two SNPs genotyped from a sample of size n. Each from a natural human population at Hardy-Weinberg genotype (and therefore each diplotype) is composed equilibrium. In this sample, a number of SNPs are geno- of two haplotypes, one from the mother and the other typed genome-wide, aimed at the identification of DNA from the father. Two haplotypes composing a diplotype sequences responsible for a complex disease. Consider come from four possible haplotypes, A 1

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A perspective on multi target drug discovery and design for complex diseases

A perspective on multi target drug discovery and design for complex diseases

growth and to facilitate its progression. Similarly to the above mentioned diseases, redundancies and complexi- ties of biological pathways often lead to compensation and resistance to targeted therapies [21–23]. Among the different classes, multi-kinase inhibitors emerged as the most exploited anti-cancer polypharmacologi- cal approach, followed by pan-inhibitors of histone dea- cetylases (HDACs). Lenvatinib in the first group is a reversible multi-tyrosine kinase receptors inhibitor that modulates the activities of vascular endothelial growth factor receptors (VEGFR) 1–3, fibroblast growth factor receptors (FGFR) 1–3, RET, mast/stem cell growth fac- tor receptor kit (SCFR), and platelet-derived growth fac- tor receptor (PDGFR) beta, all implicated in pathogenic angiogenesis, tumor growth, and cancer progression [24]. Given the broad activity profile, it was approved for the treatment of radioiodine-refractory thyroid cancers. Ner- atinib is another recently approved multi-tyrosine kinase inhibitor with an irreversible mechanism of action, which exhibits antitumor activity by targeting epidermal growth factor receptor (EGFR), and human epidermal growth factor receptor 2 (HER2), both highly expressed in sev- eral carcinomas. Taking advantage of the high sequence identity shared by EGFR and HER-2 (82%) in the ATP domain, the design of such dual-inhibitor bearing a Michael acceptor warhead was undertaken. Computa- tional studies guided the optimization of this molecule so that its warhead is positioned suitably to interact with Cys 773 of EGFR and the analogous Cys 805 of HER-2 [25].

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Complex genetic diseases: controversy over the Croesus code

Complex genetic diseases: controversy over the Croesus code

coronary artery disease) [8]. Several of these disorders show a steep decline in heritability as age-of-onset rises, implicat- ing generalized ageing processes that are not strongly influ- enced by genetic differences [8,9]. These diseases are common because of highly prevalent non-genetic influences, not because of common ‘disease alleles’ in the population. The majority of cases are not genetically determined to any meaningful extent. Such weakly disease-associated alleles as do exist can undoubtedly reach high frequencies if they are truly invisible to selection, but a key issue is the proportion of them that exert non-trivial influences on late-onset phe- notypes. An inverse relationship between the magnitude of genetic effect and allele frequency was postulated many years ago [10,11], suggesting that few variants of clinical con- sequence will be common (Figure 1b). More recently, model- ing of complex diseases by Jonathan Pritchard [12] predicts that neutral susceptibility alleles contribute little to the genetic variance underlying disease, since they tend to be either lost or close to fixation in the population. By contrast, alleles under weak selection may constitute the bulk of the genetic variance, especially at loci showing high mutation rates. This predicts extensive allelic heterogeneity underly- ing disease, although the collective frequency of these alleles may be quite high.

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Schistosoma mansoni and Schistosoma heamatobium Infection and Nutritional Status of Children in the Hydro Agricultural Complex Zone of Sourou, Burkina Faso

Schistosoma mansoni and Schistosoma heamatobium Infection and Nutritional Status of Children in the Hydro Agricultural Complex Zone of Sourou, Burkina Faso

One of the indispensable conditions to solve the African food deficits is to concentrate efforts on the use and control of water abilities. In Burkina Faso, these efforts were revealed by the construction of dams and hydro- agricultural managements. Some consider these amenities as a “weapon against hunger”, and others worry about the negative impacts on environment and health of populations especially, on the amplification of water-related diseases. Chronic infectious diseases, poverty, and malnutrition compromise the growth and development of children [1]. It is estimated that a third of the cases of low growth can be assigned to diarrhoea and equal infec- tions, even in the case of a sub-clinical infection [2]. Among the parasite infections, other than malaria, infec- tions by Schistosoma mansoni and Schistosoma heamatobium are frequent and serious in the developing coun- tries [3]. Schistosoma heamatobium is responsible of urinary complications, bladder cancer, and anaemia while Schistosoma mansoni is assigned to hepatosplenomegaly and portal hypertension. The infection can be severe with a high lethality. Some authors reported that Schistosoma mansoni is associated with a deficient nutritional statute among adults [4], and children [5] [6]. This study on Schistosoma mansoni and Schistosoma heamato- bium infections and nutritional status in the hydro-agricultural zone of Sourou aims to establish a balance of the main sanitary consequences.

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The genetic epidemiology of neurodegenerative disease

The genetic epidemiology of neurodegenerative disease

Similar to the examples in the neurodegenerative diseases, tau mutations likely represent the first and most obvious candidates in the puzzle of FTD genetics. They probably account for less than half of the genetic variance in familial FTD (64). In addition to linkage to chromosome 9q21 in a syndrome of FTD coupled with familial amyotrophic lateral sclerosis (ALS; see below), association has been observed between FTD and APOE, albeit with highly vari- able results. Interestingly, and similar to equivalent studies done in PD, a recent metaanalysis on all data published for APOE in FTD detected a significant risk effect associated with the ε2-allele but no significant results with ε4 (70). While this observation may be purely incidental, it is similar to findings on the H1-tau haplotype, which has also been associated in some FTD syndromes as well as PD. Collectively, these findings are still too preliminary to allow speculation on any functional consequences of the under- lying genetic variants in the pathogenesis of FTD. Finally, recent reports have suggested that some cases of FTD may also be caused by mutations in PSEN1 (71). However, a more rigorous proof of familial segregation and pathogenetic mechanism of these vari- ants is needed before they can be considered established.

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