Second-to-Fourth Digit Ratio Has a Non-Monotonic Impact on Altruism

Gene-culture co-evolution emphasizes the joint role of culture and genes for the emergence of altruistic and cooperative behaviors and behavioral genetics provides estimates of their relative importance. However, these approaches cannot assess which biological traits determine altruism or how. We analyze the association between altruism in adults and the exposure to prenatal sex hormones, using the second-to-fourth digit ratio. We find an inverted U-shaped relation for left and right hands, which is very consistent for men and less systematic for women. Subjects with both high and low digit ratios give less than individuals with intermediate digit ratios. We repeat the exercise with the same subjects seven months later and find a similar association, even though subjects' behavior differs the second time they play the game. We then construct proxies of the median digit ratio in the population (using more than 1000 different subjects), show that subjects' altruism decreases with the distance of their ratio to these proxies. These results provide direct evidence that prenatal events contribute to the variation of altruistic behavior and that the exposure to fetal hormones is one of the relevant biological factors. In addition, the findings suggest that there might be an optimal level of exposure to these hormones from social perspective.

Preprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer disease

DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer

Is there a subgroup of long-term evolution among patients with advanced lung cancer?: Hints from the analysis of survival curves from cancer registry data

Background: Recently, with the access of low toxicity biological and targeted therapies, evidence of the existence of a long-term survival subpopulation of cancer patients is appearing. We have studied an unselected population with advanced lung cancer to look for evidence of multimodality in survival distribution, and estimate the proportion of long-term survivors. Methods: We used survival data of 4944 patients with non-small-cell lung cancer (NSCLC) stages IIIb-IV at diagnostic, registered in the National Cancer Registry of Cuba (NCRC) between January 1998 and December 2006. We fitted one-component survival model and two-component mixture models to identify short-and long-term survivors. Bayesian information criterion was used for model selection. Results: For all of the selected parametric distributions the two components model presented the best fit. The population with short-term survival (almost 4 months median survival) represented 64% of patients. The population of long-term survival included 35% of patients, and showed a median survival around 12 months. None of the patients of short-term survival was still alive at month 24, while 10% of the patients of long-term survival died afterwards. Conclusions: There is a subgroup showing long-term evolution among patients with advanced lung cancer. As survival rates continue to improve with the new generation of therapies, prognostic models considering short-and long-term survival subpopulations should be considered in clinical research.