[Edited to add Obama’s positions on NASA and Space Exploration]
Those of you who know me already know that I support Barack Obama for president. I’ve hesitated to post this here, due to the non-political nature of this blog. However, it is long past time for me to make the case for Obama and his policies on science and research. More after the cut on his key positions and why they are important.
“no great genius was without a mixture of insanity”
“They say madness runs in our family. Some even call me mad! And why? Because I dared to dream …of my own race of atomic monsters! Atomic supermen with octagonal-shaped bodies that suck blood out of…”
-Prof. Hubert Farnsworth
To be clear, by genius I mean the ability for humans to think at the level we do. And by madness, I mean madness. A recent study suggests that we have our big brains at a high price: schizophrenia. It comes down to the massive metabolism needs that our brains have. A team led by evolutionary biologist Philipp Khaitovich of the Shanghai Institutes for Biological Sciences and the Max Planck Institute for Evolutionary Anthropology created an experiment to see how much genes that are involved in schizophrenia have evolved since humans split from chimpanzees. Continue reading
We (my lab, http://krauthammerlab.med.yale.edu) recently published on our new image search engine for biomedical images, called YIF, or Yale Image Finder. From our blog post on our web site:
We have recently released a new biomedical image search engine we call YIF. You can access it at:
You can search the actual image content of over 34,000 Open Access articles from PubMed Central. We use OCR with different levels of image correction (article and corpus) for highly accurate image text extraction.
For more details about our algorithms, we have a paper in Bioinformatics titled “Yale Image Finder (YIF): a new search engine for retrieving biomedical images“.
15 hours and 40 doctors later, a farmer has two new arms. The surgery replaced both limbs of a farmer who lost his arms in an accident in 2002. He lost both arms below the shoulder. From the Guardian article:
Christoph Höhnke, a surgeon on the transplant team, said that the complicated procedure was completed without any unforeseen problems. It involved a team of 40 doctors, nurses and assistants working together, attaching one arm and then the other. “The whole thing went according to script,” he said.
With a surgery as complicated as this, though, many things can go wrong. The patient can reject the transplants, although close matching of blood and other immunological factors can minimize this, along with drugs that can dampen the immune system’s reaction to foreign bodies. The bones may not completely graft together. It’s like recovering from two severely broken arms on top of everything else.
Nerve regeneration has come a long way, however. The man who received a single arm transplant in 2006 was able to write with the transplanted hand within a year.
“nerve regeneration” at Yale Image Finder.
Update, 7/13/2013: I’m amazed at the continued staying power of this post, considering that I had originally worked the math out for this 14 years ago. People are still commenting on this and suggesting fixes. I’m also amazed that I’ve peppered enough errors in the math and code for people to still be finding errors 5 years after the fact.
My friend Dan at Invisible Blocks came up with a great way to compute a long-running mean from the count and mean:
count += 1
mean += (x - mean) / count
I remembered that I had come up with a similar thing for standard deviation back when I was developing clustering algorithms that could use that value. It uses a power sum average, where you track the power sum as an average (divide the power sum by n) in a similar way.
Data mining is, in the most general terms, an attempt to extract patterns and knowledge from data using various types of software and techniques. Data mining is used to learn and predict. This is applied to biology, neuroscience, fraud detection, national security, and even sports.
Some of these are more successful than others. For instance, text mining has been very successful at extracting proper nouns (names, places, etc.) from text, and what might be considered the biggest success of data mining comes from text mining: internet search engines. But at the same time, text mining has been less successful at automated text summarization. Continue reading
Otherwise known as Dark Matter, WIMPs (Weakly-Interacting Massive Particles) may have had a significant role in creating the first stars, according to a paper in Physical Review Letters called “Dark Matter Capture in the first star: a Power source and a limit on Stellar Mass“. Since WIMPs are their own antiparticles, large collections of them may have annihilated each other to the point of outshining the fusion reactions of those initial stars.
The dark matter may have also provided an upper limit on the size of those initial stars. Because matter-antimatter reactions produce so much more energy than fusion reactions, those initial stars would more quickly reach their Eddington luminosity, or the upper limit of star brightness beyond which the star throws off its own mass due to excessive energy. This can be seen as a possible way that the first gases were dispersed across the universe, seeding later stars in the way that current supernovas do.
The first stars are hypothesized to have formed within reservoirs of dark matter, possibly providing the first “lumpy” features in the universe. As the stars grew in size, they accumulated dark matter through gravitation. As the dark matter accumulated at the center of the star, they began to annihilate, producing some of the first light in the universe.