Supplementary data are available at Bioinformatics online.

Imposition of a lasso penalty shrinks parameter estimates toward zero and performs continuous model selection. Lasso penalized regression is capable of handling linear regression problems where the number of predictors far exceeds the number of cases. This paper tests two exceptionally fast algorithms for estimating regression coefficients with a lasso penalty. The previously known $\ell_2$ algorithm is based on cyclic coordinate descent. Our new $\ell_1$ algorithm is based on greedy coordinate descent and Edgeworth's algorithm for ordinary $\ell_1$ regression. Each algorithm relies on a tuning constant that can be chosen by cross-validation. In some regression problems it is natural to group parameters and penalize parameters group by group rather than separately. If the group penalty is proportional to the Euclidean norm of the parameters of the group, then it is possible to majorize the norm and reduce parameter estimation to $\ell_2$ regression with a lasso penalty. Thus, the existing algorithm can be extended to novel settings. Each of the algorithms discussed is tested via either simulated or real data or both. The Appendix proves that a greedy form of the $\ell_2$ algorithm converges to the minimum value of the objective function.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS147 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Oral Candida albicans has been detected in children with early childhood caries (ECC) and has demonstrated cariogenic traits in animal models of the disease. Conversely, other studies found no positive correlation between C. albicans and caries experience in children, while suggesting it may have protective effects as a commensal organism. Thus, this study aimed to examine whether oral C. albicans is associated with ECC. Seven electronic databases were searched. The data from eligible studies were extracted, and the risk of bias was evaluated. A fixed effects model (Mantel-Haenszel estimate) was used for meta-analysis, and the summary effect measure was calculated by odds ratio (OR) and 95% confidence interval (CI). Fifteen cross-sectional studies were included for the qualitative assessment and 9 studies for meta-analysis. Twelve studies revealed higher oral C. albicans prevalence in ECC children than in caries-free children, while 2 studies indicated an equivalent prevalence. A pooled estimate, with OR = 6.51 and 95% CI = 4.94-8.57, indicated a significantly higher ECC experience in children with oral C. albicans than those without C. albicans (p < 0.01). The odds of experiencing ECC in children with C. albicans versus children without C. albicans were 5.26 for salivary, 6.69 for plaque, and 6.3 for oral swab samples. This systematic review indicates that children with oral C. albicans have >5 times higher odds of having ECC compared to those without C. albicans. Further prospective cohort studies are needed to determine whether C. albicans could be a risk factor for ECC, and whether it is dependent on different sample sources (saliva/plaque).