Plant RNAs Found in Mammals

有文章证实：植物源RNA能在人体里积累，并且影响人的基因表达。请看评论：

http://the-scientist.com/2011/09/20/plant-rnas-found-in-mammals/

我在想，这似乎为中药和很多中国饮食学说找到了根据（必须澄清：我不太了解西方的药物和饮食理论）。

population structure - the number of inferred clusters depends on sample size

Clustering methods have been used extensively to unravel cryptic population genetic structure. We investigated the effect of the number of individuals sampled in each location on the resulting number of clusters. Our study was motivated by recent results in Arabidopsis thaliana: studies in which more than one individual was sampled per location apparently have led to a much higher number of clusters than studies where only one individual was sampled in each location, as is generally done in this species. We show, using computer simulations and microsatellite data in A. thaliana, that the number of sampled individuals indeed has a strong impact on the number of resulting clusters. This effect is smaller if the sampled populations have a hierarchical structure. In most cases, sampling 5–10 individuals per population should be enough. The results argue for abandoning the concept of ‘accessions’ in partially selfing organisms.

http://onlinelibrary.wiley.com/doi/10.1111/j.1755-0998.2009.02756.x/full

meta-analysis of phynotypic effects - a case (with MCMCglmm used)

http://www.sciencedirect.com/science/article/pii/S0003347211001229
Dominance and plumage traits: meta-analysis and metaregression analysis 

Procedures of Meta-analysis and Metaregression

All meta-analyses were conducted in the statistical software S-Plus (TIBCO; http://www.tibco.com/) and R (version 2.11.1; R Development Core Team 2010), using LMMs to perform a random-effect meta-analysis (Nakagawa et al. 2007). In all analyses, we accounted for the hierarchical structure in the data (e.g. with multiple effect sizes from the same population) by including study population and species as nested random effects (Nakagawa et al. 2007). This statistical procedure allowed us to use multiple effect sizes from a study or population in the same analysis without violating the assumption of independence (Nakagawa & Hauber 2011). Our meta-analytical LMM was calculated as an intercept-only model with the restricted maximum likelihood (REML) method (nlme package; Pinheiro & Bates 2000). Reported P values are for the main effects (intercepts) only.
To determine whether a set of effect sizes was homogeneous, we calculated the residual heterogeneity Q_REML, as random-effects models were used (Nakagawa et al. 2007). When residual heterogeneity was significant, the variance among effect sizes was greater than expected from sampling error, suggesting the existence of important moderator variables. It is worth emphasizing that even though our metaregressions accounted for some moderator variables (Table 1), it is still possible that other (unaccounted) moderators could have introduced heterogeneity in the data. Furthermore, we decided not to include interaction terms among predictor variables in our metaregressions, as the models would be overparameterized (i.e. models would have too many parameters and not enough data points in each category to be robust; Ginzburg & Jensen 2004). We conducted contrast analyses (LMMs) for all metaregression models to check for the effect of different levels of an explanatory variable on the relationship between dominance and plumage. We show the results of contrast analyses only if the difference between levels of a variable (contrasts) was statistically significant (all other results are in Table A2 in Appendix 2).
We also conducted a randomization test to evaluate the importance of the variance components of our random factors (i.e. study and species). If a variance component was significantly different from zero, it indicated the existence of either study or species effects, the latter of which may, but does not necessarily, imply phylogenetic signal in the data (cf. Hadfield & Nakagawa 2010). We tested the null hypothesis that the variance component = 0, against the alternative hypothesis that the variance component > 0 (Nakagawa & Schielzeth 2010). We randomized the original effect size vector 100 000 times, each followed by fitting the meta-analytical LMM to estimate randomized random factor variance components. The P value was determined as the proportion of randomizations that yielded a variance component larger than or equal to the variance component of the original data. Furthermore, we conducted a phylogenetic meta-analysis described in Hadfield & Nakagawa (2010) to account for the lack of independence across species caused by their evolutionary relationships, using the MCMCglmm package in R (Hadfield, 2010 J.D. Hadfield, MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package, Journal of Statistical Software 33 (2010), pp. 1–22. View Record in Scopus | Cited By in Scopus (45)Hadfield 2010). As our conclusions did not change with the use of the phylogenetic meta-analysis, we only present results based on the general meta-analysis (the results of the phylogenetic meta-analysis can be found in Appendix 3).

knowledgeblog will change the academic publising process

knowledgeblog has put forward a blog-based academic publication strategy. That is very interesting and attractive. I would like to take part in.

But, it looks it is just on the its beginning. No real case/example there. 

http://knowledgeblog.org/