Introduction Yoga is a popular form of complementary and alternative treatment. It is practiced both in developing and developed countries. Use of yoga for various bodily ailments is recommended in ancient ayvurvedic (ayus =life, veda =knowledge) texts and is being increasingly investigated scientifically. Many patients and yoga protagonists claim that it is useful in sexual disorders. We are interested in knowing if it works for patients with premature ejaculation (PE) and in comparing its efficacy with fluoxetine, a known treatment option for PE. Aim To know if yoga could be tried as a treatment option in PE and to compare it with fluoxetine. Methods A total of 68 patients (38 yoga group; 30 fluoxetine group) attending the outpatient department of psychiatry of a tertiary care hospital were enrolled in the present study. Both subjective and objective assessment tools were administered to evaluate the efficacy of the yoga and fluoxetine in PE. Three patients dropped out of the study citing their inability to cope up with the yoga schedule as the reason. Main Outcome Measure Intravaginal ejaculatory latencies in yoga group and fluoxetine control groups. Results We found that all 38 patients (25–65.7%=good, 13–34.2%=fair) belonging to yoga and 25 out of 30 of the fluoxetine group (82.3%) had statistically significant improvement in PE. Conclusions Yoga appears to be a feasible, safe, effective and acceptable nonpharmacological option for PE. More studies involving larger patients could be carried out to establish its utility in this condition.

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Discriminative patterns can provide valuable insights into datasets with class labels, that may not be available from the individual features or the predictive models built using them. Most existing approaches work efficiently for sparse or low-dimensional datasets. However, for dense and highdimensional datasets, they have to use high thresholds to produce the complete results within limited time, and thus, may miss interesting low-support patterns. In this paper, we address the necessity of trading off the completeness of discriminative pattern discovery with the efficient discovery of lowsupport discriminative patterns from such datasets. We propose a family of anti-monotonic measures named SupMaxK that organize the set of discriminative patterns into nested layers of subsets, which are progressively more complete in their coverage, but require increasingly more computation. In particular, the member of SupMaxK with K = 2, named SupMaxPair , is suitable for dense and high-dimensional datasets. Experiments on both synthetic datasets and a cancer gene expression dataset demonstrate that there are low-support patterns that can be discovered using SupMaxPair but not by existing approaches. Furthermore, we show that the low-support discriminative patterns that are only discovered using SupMaxPair from the cancer gene expression dataset are statistically significant and biologically relevant. This illustrates the complementarity of SupMaxPair to existing approaches for discriminative pattern discovery. The codes and dataset for this paper are available at http://vk.cs.umn.edu/SMP/.

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