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Ovarian cancer is the third most common gynaecological malignancy. Changes in circadian rhythms such as bright light exposure may affect female reproductive physiology. Night shift work is associated with higher risks of developing gynaecological cancers. In addition, the season of birth is also suggested as an important environmental risk factor for developing gynaecological cancers. Melatonin may play an important role in this association as a marker of circadian rhythms. Serum from 96 women with ovarian cancer and 40 healthy women were collected and the level of melatonin was measured. In addition 277 women with ovarian cancer and 1076 controls were retrospectively collected for season of birth analysis over seven years. The serum levels of melatonin were significantly lower in women with ovarian cancer compared with healthy women (p<0.05). However there was no difference in melatonin levels in perimenopausal and postmenopausal patients. In addition, there is no statistically significant difference in seasonal distribution of birth between ovarian cancer patients and the control group. The melatonin levels in ovarian cancer patients and controls were not associated with the season of birth. Our results demonstrate the lower serum levels of melatonin in ovarian cancer patients which may contribute to the pathogenesis of ovarian cancer. The incidence of ovarian cancer was not associated with the season of birth. The serum levels of melatonin do not appear to be associated with season of birth in ovarian cancer patients.

Interactive analytics increasingly involves querying for quantiles over sub-populations of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles, but summary merge times can be a bottleneck during aggregation. We show how a compact and efficiently mergeable quantile sketch can support aggregation workloads. This data structure, which we refer to as the moments sketch, operates with a small memory footprint (200 bytes) and computationally efficient (50ns) merges by tracking only a set of summary statistics, notably the sample moments. We demonstrate how we can efficiently estimate quantiles using the method of moments and the maximum entropy principle, and show how the use of a cascade further improves query time for threshold predicates. Empirical evaluation shows that the moments sketch can achieve less than 1 percent quantile error with 15× less overhead than comparable summaries, improving end query time in the MacroBase engine by up to 7× and the Druid engine by up to 60×. *

Tens of thousands of genotype-phenotype associations have been discovered to date, yet not all of them are easily accessible to scientists. Here, we describe GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms. Our information extraction system helps curators by automatically collecting over 6,000 associations from open-access publications with an estimated recall of 60–80% and with an estimated precision of 78–94% (measured relative to existing manually curated knowledge bases). This system represents a fully automated GWAS curation effort and is made possible by a paradigm for constructing machine learning systems called data programming. Our work represents a step towards making the curation of scientific literature more efficient using automated systems.

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