Microbial production of uracil by an isolated Methylobacterium sp. WJ4 using methanol.

In this study, we report the production of uracil from methanol by an isolated methylotrophic bacterium, Methylobacterium sp. WJ4. The use of methanol as alternative carbon feedstock is attractive option in biotechnology. As a feedstock of biotechnological processes, methanol has distinct advantages over methane. This is not only due to physical and chemical considerations, but also to the properties of the pertinent organisms. Besides, with a wide array of biological activities and synthetic accessibility, uracil is considered as privileged structures in drug discovery. Uracil analogues have been applied to treatments of patients with cancer or viral infections. In this respect, it is meaningful to produce uracil using methanol. The effect of process parameters and methanol concentration for uracil production were investigated and optimized. Uracil production was remarkably increased to 5.76mgg cell dry weight-1 in optimized condition. The results were significant for further understanding of methylotrophic bacteria on uracil production.

Production of uracil from methane by a newly isolated Methylomonas sp. SW1.

Methane is an abundant, inexpensive one-carbon feedstock and one of the most powerful greenhouse gases. Because it does not compete with food demand, it is considered a promising carbon feedstock for the production of valuable products using methanotrophic bacteria. Here, we isolated a novel methanotrophic bacterium, Methylomonas sp. SW1, from a sewage sample obtained from Wonju City Water Supply Drainage Center, Republic of Korea. The conditions for uracil production by Methylomonas sp. SW1, such as Cu2+ concentration and temperature were investigated and optimized. As a result, Methylomonas sp. SW1 produced uracil from methane as a sole carbon source with a titer of 2.1mg/L in 84h without genetic engineering under the optimized condition. The results in this study demonstrate the feasibility of using Methylomonas sp. SW1 for the production of uracil from methane. This is the first report of uracil production from gas feedstock by methanotrophic bacteria.

Cluster analysis reveals risk factors for repeated suicide attempts in a multi-ethnic Asian population.

This study explores underlying patterns in suicide risk factors using data mining techniques. Medical records of suicide attempters who were admitted to a teaching hospital in January 2004 - December 2006 were studied. Cluster analysis revealed hidden patterns for repeated and single attempters (n=418). Repeated attempters had a more complex clinical picture. Symptoms of psychotic illness, borderline personality disorder, and psychosomatic complaints of insomnia and headaches, reports of adverse life events such as unemployment, divorce and quarrels, experience of negative feelings, and usage of alcohol were associated with risk of repeated overdoses with benzodiazepines and paracetamol. The findings have implications for suicide assessments and interventions.

Anonymous indexing of health conditions for a similarity measure.

A health social network is an online information service which facilitates information sharing between closely related members of a community with the same or a similar health condition. Over the years, many automated recommender systems have been developed for social networking in order to help users find their communities of interest. For health social networking, the ideal source of information for measuring similarities of patients is the medical information of the patients. However, it is not desirable that such sensitive and private information be shared over the Internet. This is also true for many other security sensitive domains. A new information-sharing scheme is developed where each patient is represented as a small number of (possibly disjoint) d-words (discriminant words) and the d-words are used to measure similarities between patients without revealing sensitive personal information. The d-words are simple words like "food,'' and thus do not contain identifiable personal information. This makes our method an effective one-way hashing of patient assessments for a similarity measure. The d-words can be easily shared on the Internet to find peers who might have similar health conditions.