The anatomy of friendship:neuroanatomic homophily of the social brain among classroom friends

Homophily refers to the tendency to like similar others. Here, we ask if homophily extends to brain structure. Specifically: do children who like one another have more similar brain structures? We hypothesized that neuroanatomic similarity tied to friendship is most likely to pertain to brain regions that support social cognition. To test this hypothesis, we analyzed friendship network data from 1186 children in 49 classrooms. Within each classroom, we identified “friendship distance”—mutual friends, friends-of-friends, and more distantly connected or unconnected children. In total, 125 children (mean age = 7.57 years, 65 females) also had good quality neuroanatomic magnetic resonance imaging scans from which we extracted properties of the “social brain.” We found that similarity of the social brain varied by friendship distance: mutual friends showed greater similarity in social brain networks compared with friends-of-friends (β = 0.65, t = 2.03, P = 0.045) and even more remotely connected peers (β = 0.77, t = 2.83, P = 0.006); friends-of-friends did not differ from more distantly connected peers (β = −0.13, t = −0.53, P = 0.6). We report that mutual friends have similar “social brain” networks, adding a neuroanatomic dimension to the adage that “birds of a feather flock together.

Genetic associations with childhood brain growth, defined in two longitudinal cohorts

Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta-analysis identified a genome-wide significant intergenic SNP (rs12386571, P = 9.09 × 10-9 ), near AKR1B10. This gene is part of the aldo-keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study

Prevalence of liver fluke (Fasciola hepatica) in wild Red Deer (Cervus elaphus): coproantigen ELISA is a practicable alternative to faecal egg counting for surveillance in remote populations

Red deer (Cervus elaphus) are hosts of liver fluke (Fasciola hepatica); yet, prevalence is rarely quantified in wild populations. Testing fresh samples from remote regions by faecal examination (FE) can be logistically challenging; hence, we appraise frozen storage and the use of a coproantigen ELISA (cELISA) for F. hepatica surveillance. We also present cELISA surveillance data for red deer from the Highlands of Scotland. Diagnoses in faecal samples (207 frozen, 146 fresh) were compared using a cELISA and by FE. For each storage method (frozen or fresh), agreement between the two diagnostics was estimated at individual and population levels, where population prevalence was stratified into cohorts (e.g., by sampling location). To approximate sensitivity and specificity, 65 post-slaughter whole liver examinations were used as a reference. At the individual level, FE and cELISA diagnoses agreed moderately (κfrozen = 0.46; κfresh = 0.51), a likely reflection of their underlying principles. At the population level, FE and cELISA cohort prevalence correlated strongly (Pearson’s R = 0.89, p < 0.0001), reflecting good agreement on relative differences between cohort prevalence. In frozen samples, prevalence by cELISA exceeded FE overall (42.8% vs. 25.8%) and in 9/12 cohorts, alluding to differences in sensitivity; though, in fresh samples, no significant difference was found. In 959 deer tested by cELISA across the Scottish Highlands, infection prevalence ranged from 9.6% to 53% by sampling location. We highlight two key advantages of cELISA over FE: i) the ability to store samples long term (frozen) without apparent loss in diagnostic power; and ii) reduced labour and the ability to process large batches. Further evaluation of cELISA sensitivity in red deer, where a range of fluke burdens can be obtained, is desirable. In the interim, the cELISA is a practicable diagnostic for F. hepatica surveillance in red deer, and its application here has revealed considerable geographic, temporal, sex and age related differences in F. hepatica prevalence in wild Scottish Highland red deer

Prediction of avian influenza A binding preference to human receptor using conformational analysis of receptor bound to hemagglutinin

<p>Abstract</p> <p>Background</p> <p>It is known that the highly pathogenic avian influenza A virus H5N1 binds strongly and with high specificity to the avian-type receptor by its hemagglutinin surface protein. This specificity is normally a barrier to viral transmission from birds to humans. However, strains may emerge with mutated hemagglutinin, potentially changing the receptor binding preference from avian to human-type. This hypothesis has been proven correct, since viral isolates from Vietnam and Thailand have been found which have increased selectivity toward the human cell receptor. The change in binding preference is due to mutation, which can be computationally modelled. The aim of this study is to further explore whether computational simulation could be used as a prediction tool for host type selectivity in emerging variants.</p> <p>Results</p> <p>Molecular dynamics simulation was employed to study the interactions between receptor models and hemagglutinin proteins from H5N1 strains A/Duck/Singapore/3/97, mutated A/Duck/Singapore/3/97 (Q222L, G224S, Q222L/G224S), A/Thailand/1(KAN-1)/2004, and mutated A/Thailand/1(KAN-1)/2004 (L129V/A134V). The avian receptor was represented by Siaα(2,3)Gal substructure and human receptor by Siaα(2,6)Gal. The glycoside binding conformation was monitored throughout the simulations since high selectivity toward a particular host occurs when the sialoside bound with the near-optimized conformation.</p> <p>Conclusion</p> <p>The simulation results showed all hemagglutinin proteins used the same set of amino acid residues to bind with the glycoside; however, some mutations alter linkage preferences. Preference toward human-type receptors is associated with a positive torsion angle, while avian-type receptor preference is associated with a negative torsion angle. According to the conformation analysis of the bound receptors, we could predict the relative selectivity in accordance with <it>in vitro </it>experimental data when disaccharides receptor analogs were used.</p

Global gene expression profiling of Plasmodium falciparum in response to the anti-malarial drug pyronaridine

<p>Abstract</p> <p>Background</p> <p>Pyronaridine (PN) and chloroquine (CQ) are structurally related anti-malarial drugs with primarily the same mode of action. However, PN is effective against several multidrug-resistant lines of <it>Plasmodium falciparum</it>, including CQ resistant lines, suggestive of important operational differences between the two drugs.</p> <p>Methods</p> <p>Synchronized trophozoite stage cultures of <it>P. falciparum </it>strain K1 (CQ resistant) were exposed to 50% inhibitory concentrations (IC₅₀) of PN and CQ, and parasites were harvested from culture after 4 and 24 hours exposure. Global transcriptional changes effected by drug treatment were investigated using DNA microarrays.</p> <p>Results</p> <p>After a 4 h drug exposure, PN induced a greater degree of transcriptional perturbation (61 differentially expressed features) than CQ (10 features). More genes were found to respond to 24 h treatments with both drugs, and 461 features were found to be significantly responsive to one or both drugs across all treatment conditions.</p> <p>Filtering was employed to remove features unrelated to primary drug action, specifically features representing genes developmentally regulated, secondary stress/death related processes and sexual stage development. The only significant gene ontologies represented among the 46 remaining features after filtering relate to host exported proteins from multi-gene families.</p> <p>Conclusions</p> <p>The malaria parasite's molecular responses to PN and CQ treatment are similar in terms of the genes and pathways affected. However, PN appears to exert a more rapid response than CQ. The faster action of PN may explain why PN is more efficacious than CQ, particularly against CQ resistant isolates. In agreement with several other microarray studies of drug action on the parasite, it is not possible, however, to discern mechanism of drug action from the drug-responsive genes.</p

Iterative pruning PCA improves resolution of highly structured populations

BACKGROUND: Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming. RESULTS: A novel population structure analysis algorithm called iterative pruning PCA (ipPCA) was developed which assigns individuals to subpopulations and infers the total number of subpopulations present. Genotypic data from simulated and real population datasets with different degrees of structure were analyzed. For datasets with simple structures, the subpopulation assignments of individuals made by ipPCA were largely consistent with the STRUCTURE, BAPS and AWclust algorithms. On the other hand, highly structured populations containing many closely related subpopulations could be accurately resolved only by ipPCA, and not by other methods. CONCLUSION: The algorithm is computationally efficient and not constrained by the dataset complexity. This systematic subpopulation assignment approach removes the need for prior population labels, which could be advantageous when cryptic stratification is encountered in datasets containing individuals otherwise assumed to belong to a homogenous population

Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure

<p>Abstract</p> <p>Background</p> <p>The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis.</p> <p>Results</p> <p>A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA.</p> <p>Conclusions</p> <p>The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from <url>http://www4a.biotec.or.th/GI/tools/ippca</url>.</p

Effect of increasing fruit and vegetable intake by dietary intervention on nutritional biomarkers and attitudes to dietary change : a randomised trial

This work was funded by The Scottish Government Rural and Environmental Science and Analytical Sciences Division (RESAS) and supported by the Rank Prize Funds.Peer reviewedPublisher PD

Genetic analysis of Thai cattle reveals a Southeast Asian indicine ancestry

Cattle commonly raised in Thailand have characteristics of [i]Bos indicus[/i] (zebu). We do not know when or how cattle domestication in Thailand occurred, and so questions remain regarding their origins and relationships to other breeds. We obtained genome-wide SNP genotypic data of 28 bovine individuals sampled from four regions: North (Kho-Khaolampoon), Northeast (Kho-Isaan), Central (Kho-Lan) and South (Kho-Chon) Thailand. These regional varieties have distinctive traits suggestive of breed-like genetic variations. From these data, we confirmed that all four Thai varieties are [i]Bos indicus[/i] and that they are distinct from other indicine breeds. Among these Thai cattle, a distinctive ancestry pattern is apparent, which is the purest within Kho-Chon individuals. This ancestral component is only present outside of Thailand among other indicine breeds in Southeast Asia. From this pattern, we conclude that a unique [i]Bos indicus[/i] ancestor originated in Southeast Asia, and native Kho-Chon Thai cattle retain the signal of this ancestry with limited admixture of other bovine ancestors