matterhacker

Cool article over at the New Yorker about how humans process numerics naturally….

Here’s a story on yahoo about an Irish man who had his ssight restored after surgeons implanted his son’s tooth in his eye socket and stuck a lens on it. This cannot be true. Update: Oh hell yes it is! Pretty amazing procedure. I found out a bit more about it. It’s called the Osteo-Odonto-Keratoprosthesis (OOKP) procedure. That pdf has all the details and a pictorial description is below:

This story from ABC appeared on the Digg front page today.  It’s about an autistic woman who has proven that autistic persons can communicate quite readily and intelligently.  All that she needed was a method of communication that she could work with.  In this case her parents got together with a psychologist to design a keyboard with pictures and symbols that allowed her to type out what she wanted to say.  Great read!  It reminds me of silentmiaow, her blog and youtube entries.  She too has severe autism and has found typing to be the only way to effectively communicate with non-autistic people.  Amazing to hear the thoughts, emotions and opinions of people with autism.  People that society thought of as unintelligent in the past.

Wired has a nice picturesque story about the Stanford Linear Accelerator (SLAC). Cool place. I did some research there at some point in my career. The whole complex is like something out of a James Bond movie. Catwalks and all kinds of crazy electronics all over. It’s probably most useful as a synchrotron source. You can do a lot of really neat stuff with high energy x-rays… especially in relation to structural molecular biology. I probably should have stayed in that line of research. It’s a lot more applicable.

This story popped up on Slashdot today. Two artists have played back the recordings of brainwave activity of people in REM sleep on robots. Essentially the robot will act out the “in-dream” actions of the sleeping person. Pretty cool! Original story is here. A link to the video is below:

An article from National Geographic about our impact on the oceans.  From the article, an ocean mapping study done by Ben Halpern of U.C. Santa Barbara has shown that:

” Every area of the oceans is feeling the effects of fishing, pollution, or human-caused global warming, the study says, and some regions are being affected by all of these factors and more.”

BBC reports on another interesting article in Nature today.  However, I can’t seem to find it in today’s issue of Nature.  Scientists from McGill University have found a way to increase the immune system response of mice.  The work comes from the lab of Prof. Sonenberg.  From the BBC article, the researchers turned off 2 genes responsible for repressing interferon production in mice cells and thereby boosted the immune response of the mice.  However, they say that the method cannot be used in human cells for now.

Another SVMs applied to DNA microarray data project.  Looks like it’s from a collaboration called MLExAI between the University of Hartford and Central Connecticut State University that is an information exchange on teaching machine learning and AI material.

A really good (but basic) intro on Support Vector Machines from Nature Biotechnology written by William Noble. Explains the basics of SVMs using DNA Microarray data analysis. From the article:

“…to understand the essence of SVM classification, one needs only to grasp four basic concepts: (i) the separating hyperplane, (ii) the maximum-margin hyperplane, (iii) the soft margin and (iv) the kernel function.”

Good read for a conceptual understanding… but no math.

A paper published in PNAS on using SVMs to analyze DNA microarray data:

“We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.”