by: Michelle Dunn and Vinay Pai

Tough scientific problems often require a mixture of ideas and tools from a group of people with diverse expertise to tackle them. Biomedical science is no exception. Biomedical problems that are data-intensive or computational-heavy may be theoretically possible to be solved, but only practically possible for a few people and only within a team. Quantitative and computational advances – in analytic methods, algorithms development, the implementation of algorithms, and the usage of high performance computing or cloud technologies – can make the difference between computing a solution in days or years.

To encourage members of diverse scientific groups to work together on developing innovative approaches to tackling biomedical problems, the National Institutes of Health (NIH) and the National Science Foundation (NSF) are teaming up to jointly sponsor an Ideas Lab. An Ideas Lab is an intensive, interactive, residential workshop designed to bring together participants from a diverse range of disciplines together to immerse themselves deeply in an environment conducive to collaboration and creativity, developing research programs around an important challenge. This Ideas Lab, on Interdisciplinary Approaches to Biomedical Data Science Challenges, is a collaboration between the NIH Big Data to Knowledge program and the NSF Division of Mathematical Science. It will bring together biomedical scientists with mathematicians, statisticians, and other computational and quantitative thinkers (we welcome quantitative scientists from application areas such as finance, high energy physics, and astronomy) to work on topics related to Precision Medicine.

The outcome of the workshop is expected to be multi-disciplinary research ideas that are novel, innovative, and risky. It is expected that some of the research ideas will compete successfully for funds to further nurture the collaborations and perform the preliminary research to evaluate the feasibility. The Ideas Lab, to be hosted by the Statistical and Applied Math Sciences Institute (SAMSI), is planned for July 20-24, 2015.

Applications to the Ideas Lab are being solicited now through May 25; more information, including the application, can be found here. Prior collaborations with computational or quantitative scientists are not necessary for biomedical scientists – only an appreciation of the value of the intellectual contributions of other areas. Similarly, prior work in biomedical sciences is not a necessity for quantitative scientists – only work in data-intensive or computation-heavy areas of science and a desire to contribute to improving health.

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## I think you may find this

I think you may find this information on mathematically calculating the accurate value of geometric Pi interesting.

The mathematical proof of the accurate value of Pi is:

applying the equation,

"inverse sin delta theta radians = Pi x theta degrees/6y"

where:

1. delta theta is 1/12 of 2 x Pi radians;

2. theta degrees is 1/12 of 360 degrees = 30 degrees

3. and 'y' is 0.5.

When the value of Pi = 3.141592654 is applied to this equation,

inverse sin 0.523598778 = 3.141592654 x 30 degrees/(6 x 0.5)

31.57396133 degrees = 94.24777961 degrees/3

31.57396133 degrees = 31.415926654 degrees

the left hand side of the equation is not equal to the right hand side.

Now when Pi is three (3.0), note the answer:

inverse sin 0.5 = 3.0 x 30/(6 x 0.5)

30 degrees = 90/3

30 degrees = 30 degrees

the left hand side of the equation IS EQUAL to the right hand side. This demonstrates that Pi is 3.0. It is also interesting to note that above equation is precisely accurate at 1/12 of the circle at exactly 30 degrees. For further proofs of Pi = 3.0 through experiments, see www.thegreatdesignbook.com.

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