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The etiology of addiction has not yet been fully elucidated. The ratio between the length of the second and fourth fingers (2D:4D ratio) has been linked with prenatal androgen concentrations, but also with addictive behvaiors. Aim: The present study aimed to evaluate the differences of 2D:4D ratio of individuals with cannabis and heroin addiction by examining them together with the control group. A total of sixty two male patients (33 opiate use disorder and 29 cannabis use disorder) with substance use disorder and the twenty-nine healthy controls were included in the present investigation. We obtained the lengths of 2D and 4D of the subjects by using sensitive calipers and calculated the 2D:4D. Heroin-addicted patients had lower 2D:4D ratio in in the right hand (significant difference between control group) ( p  < 0.001), there was no significant difference found between heroin-cannabis ( p  = 0.242) and control-cannabis 2D:4D ratios ( p  < 0.06). In the left hand, it was significant between the heroin-control groups ( p  < 0.037) and the cannabis-control groups ( p  < 0.023), while it was not significant between the heroin-cannabis groups ( p  = 1). In conclusion, we suggest that heroin-and cannabis addicted patients seem to have a lower ratio of 2D:4D compared to healthy control subjects. Our findings can be considered promising as to whether prenatal hormonal factors are important in the etiopathogenesis of addiction.

Rapid development and increasing popularity of gene expression microarrays have resulted in a number of studies on the discovery of co-regulated genes. One important way of discovering such co-regulations is the query-based search since gene co-expressions may indicate a shared role in a biological process. Although there exist promising query-driven search methods adapting clustering, they fail to capture many genes that function in the same biological pathway because microarray datasets are fraught with spurious samples or samples of diverse origin, or the pathways might be regulated under only a subset of samples. On the other hand, a class of clustering algorithms known as biclustering algorithms which simultaneously cluster both the items and their features are useful while analyzing gene expression data, or any data in which items are related in only a subset of their samples. This means that genes need not be related in all samples to be clustered together. Because many genes only interact under specific circumstances, biclustering may recover the relationships that traditional clustering algorithms can easily miss. In this chapter, we briefly summarize the literature using biclustering for querying co-regulated genes. Then we present a novel biclustering approach and evaluate its performance by a thorough experimental analysis.

Viruses of influenza A subtype H7 can be highly pathogenic and periodically infect humans. For example, there have been numerous outbreaks of H7 in the Americas and Europe since 1996. More recently, a reassortant H7N9 has emerged among humans and birds during 2013-2014 in China, Taiwan and Hong Kong. This H7N9 genome consists of genetic segments that assort with H7 and H9 viruses previously circulating in chickens and wild birds in China and ducks in Korea. Epidemic risk modellers have used agricultural, climatic and demographic data to predict that the virus will spread to northern Vietnam via poultry. To shed light on the traffic of H7 viruses in general, we examine genetic segments of influenza that have assorted with many strains of H7 viruses dating back to 1902. We focus on use cases from the United States, Italy and China. We apply a novel metric, betweenness, an associated phylogenetic visualization technique, transmission networks, and compare these with another technique, route mapping. In contrast to traditional views, our results illustrate that segments that assort with H7 viruses are spread frequently between the Americas and Eurasia. In summary, genetic segments that historically assort with H7 influenza viruses have been spread from China to: Australia, Czech Republic, Denmark, Egypt, Germany, Hong Kong, Italy, Japan, Mongolia, the Netherlands, New Zealand, Pakistan, South Africa, South Korea, Spain, Sweden, the UK, the US, and Vietnam., (© The Willi Hennig Society 2015.)