%polynova_2way: A SAS macro for implementation of mixed models for metabolomics data

The generation of large metabolomic data sets has created a high demand for software that can fit statistical models to one-metabolite-at-a-time on hundreds of metabolites. We provide the %polynova_2way macro in SAS to identify metabolites differentially expressed in study designs with a two-way factorial treatment and hierarchical design structure. For each metabolite, the macro calculates the least squares means using a linear mixed model with fixed and random effects, runs a 2-way ANOVA, corrects the P-values for the number of metabolites using the false discovery rate or Bonferroni procedure, and calculate the P-value for the least squares mean differences for each metabolite. Finally, the %polynova_2way macro outputs a table in excel format that combines all the results to facilitate the identification of significant metabolites for each factor. The macro code is freely available in the Supporting Information.

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

We present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and LATEX formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including 1) customised implementations of statistical tests, such as the Wilcoxon Rank-Sum test and the Holm-Bonferroni procedure, for comparing performances of optimisation algorithms and automatically generate result tables in PDF and LaTeX formats; 2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; 3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation operator. each testbed function. Moreover, this article comments on the current state of the literature in stochastic optimisation and highlights similarities shared by modern metaheuristics inspired by nature. It is argued that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.

Cancer Health Literacy and Willingness to Participate in Cancer Research and Donate Bio-Specimens

Although it has been well documented that poor health literacy is associated with limited participation in cancer clinical trials, studies assessing the relationships between cancer health literacy (CHL) and participation in research among diverse populations are lacking. In this study, we examined the relationship between CHL and willingness to participate in cancer research and/or donate bio-specimens (WPRDB) among African Americans, Latinos, and Whites. Participants completed the Cancer Health Literacy Test and the Multidimensional Cancer Literacy Questionnaire. Total-scale and subscale scores, frequencies, means, and distributions were computed. Analyses of variance, the Bonferroni procedure, and the Holm method were used to examine significant differences among groups. Cronbach’s alphas estimated scales’ internal consistency reliability. Significant interactions were found between race/ethnicity, gender, and CHL on WPRDB scales and subscale scores, even after education and age were taken into account. Our study confirms that CHL plays an important role that should be considered and researched further. The majority of participants were more willing to participate in non-invasive research studies (surveys, interviews, and training) or collection of bio-specimens (saliva, check cells, urine, and blood) and in studies led by their own healthcare providers, and local hospitals and universities. However, participants were less willing to participate in more-invasive studies requiring them to take medications, undergo medical procedures or donate skin/tissues. We conclude that addressing low levels of CHL and using community-based participatory approaches to address the lack of knowledge and trust about cancer research among diverse populations may increase not only their willingness to participate in research and donate bio-specimens, but may also have a positive effect on actual participation rates.