Tag Archives: science explained

So What are Genetic Algorithms?

Genetic Algorithms: Cool Name and Damn Simple is a very nice explanation with python code of genetic algorithms.

What Can Genetic Algorithms Do?
In a word, genetic algorithms optimize. They can find better answers to a question, but not solve new questions. Given the definition of a car, they might create a better car, but they’ll never give you an airplane.
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For each generation we’ll take a portion of the best performing individuals as judged by our fitness function. These high-performers will be the parents of the next generation.

We’ll also randomly select some lesser performing individuals to be parents, because we want to promote genetic diversity. Abandoning the metaphor, one of the dangers of optimization algorithms is getting stuck at a local maximum and consequently being unable to find the real maximum. By including some individuals who are not performing as well, we decrease our likelihood of getting stuck.

How Marbles Are Made

More posts on how things work: Why do We Sleep? – Why is it Colder at Higher Elevations? – What Are Flowers For?

Learning About the Moon

Planetary scientist Jennifer Heldmann discusses the Moon. From Fora.tv which has a wide selection of great webcasts.

How Aerobic Exercise Suppresses Appetite

How aerobic exercise suppresses appetite

Those of you who run, bike, swim, or otherwise engage in aerobic exercise have probably noticed that in spite of burning scads of calories during your chosen activity, the last thing you feel when you’re finished is hungry.
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The researchers discovered that aerobic exercise produces increased peptide YY levels while lowering ghrelin, leading to decreased appetite. Weight training was associated with a decrease in ghrelin, but no change in peptide YY, meaning that there was a net suppression of appetite, but not to the same degree as observed with treadmill training. In both cases, changes in appetite lasted for about two hours.

I know I find this to be true for me.

How We Found the Missing Memristor

How We Found the Missing Memristor By R. Stanley Williams

For nearly 150 years, the known fundamental passive circuit elements were limited to the capacitor (discovered in 1745), the resistor (1827), and the inductor (1831). Then, in a brilliant but underappreciated 1971 paper, Leon Chua, a professor of electrical engineering at the University of California, Berkeley, predicted the existence of a fourth fundamental device, which he called a memristor. He proved that memristor behavior could not be duplicated by any circuit built using only the other three elements, which is why the memristor is truly fundamental.
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the memristor’s potential goes far beyond instant-on computers to embrace one of the grandest technology challenges: mimicking the functions of a brain. Within a decade, memristors could let us emulate, instead of merely simulate, networks of neurons and synapses. Many research groups have been working toward a brain in silico: IBM’s Blue Brain project, Howard Hughes Medical Institute’s Janelia Farm, and Harvard’s Center for Brain Science are just three. However, even a mouse brain simulation in real time involves solving an astronomical number of coupled partial differential equations. A digital computer capable of coping with this staggering workload would need to be the size of a small city, and powering it would require several dedicated nuclear power plants.

How Antibiotics Kill Bacteria

Since the first antibiotics reached the pharmacy in the 1940s, researchers discovered that they target various pieces of machinery in bacterial cells, disrupting the bacteria’s ability to build new proteins, DNA, or cell wall. But these effects alone do not cause death, and a complete explanation of what actually kills bacteria after they are exposed to antibiotics has eluded scientists.
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The group found that all bactericidal antibiotics, regardless of their initial targets inside bacteria, caused E. coli to produce unstable chemicals called hydroxyl radicals. These compounds react with proteins, DNA, and lipids inside cells, causing widespread damage and rapid death for the bacteria.
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With the results of these two experiments, the researchers were able to identify three major processes implicated in gentamicin-induced cell death: protein transport, a stress response triggered by abnormal proteins in the cell membrane, and a metabolic stress response.

Do Breast Tumors go Away on Their Own?

Authors of a new study hope to begin a debate challenging the conventional wisdom about early detection of breast cancer. In an article in today’s Archives of Internal Medicine, they ask: Do breast tumors ever go away on their own?

Researchers of this controversial article note that one type of cancer found through screening — a rare childhood tumor, called neuroblastoma — sometimes disappears. In the new article, researchers try to learn if the same phenomenon occurs with invasive breast cancers found with mammograms

The Natural History of Invasive Breast Cancers Detected by Screening Mammography

Conclusions: Because the cumulative incidence among controls never reached that of the screened group, it appears that some breast cancers detected by repeated mammographic screening would not persist to be detectable by a single mammogram at the end of 6 years. This raises the possibility that the natural course of some screen-detected invasive breast cancers is to spontaneously regress.

As with so much medical research the results are not completely clear. Studies need to be followed by more studies, which often lead to more studies. As long as progress is being made this is a perfectly reasonable course of scientific inquiry. And even if progress is not being made this can be perfectly reasonable – finding answers can be hard.

HHMI on Science 2.0: Information Revolution

The Howard Hughes Medical Institute does great things for science and for open science. They have an excellent article in their HHMI Bulletin – Science 2.0: You Say You Want a Revolution?

Cross-pollination among research disciplines is in fact at the core of many other popular science blogs. Michael Eisen, an HHMI investigator at the University of California, Berkeley, is an avid blog reader who particularly enjoys John Hawks’ site on paleoanthropology, genetics, and evolution. A recent post there discussed a new sequencing of Neanderthal mitochondrial DNA. “It’s like a conduit into another whole world,” says Eisen.
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The current extreme of collaboration via Science 2.0 is OpenWetWare.org. Begun in 2003 by Austin Che, who was then a computer science and biology graduate student at MIT, this biological-engineering Website uses the wiki model to showcase protocols and lab books: everything is open and can be edited by any of its 4,000 members.
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“Most publishers wish open access would go away,” says Brown. It won’t. Major research-funding organizations, including NIH, HHMI, and the Wellcome Trust, now require their grantees to post their findings on openaccess Websites such as PLoS or PubMed Central within 12 months of publication in traditional journals. Publishers are pushing back, however, and in September, the House Judiciary Committee began holding hearings on whether the federal government should be allowed to require grantees to submit accepted papers to a free archive.

von Neumann Architecture and Bottleneck

We each use computers a great deal (like to write this blog and read this blog) but often have little understanding of how a computer actually works. This post gives some details on the inner workings of your computer.
What Your Computer Does While You Wait

People refer to the bottleneck between CPU and memory as the von Neumann bottleneck. Now, the front side bus bandwidth, ~10GB/s, actually looks decent. At that rate, you could read all of 8GB of system memory in less than one second or read 100 bytes in 10ns. Sadly this throughput is a theoretical maximum (unlike most others in the diagram) and cannot be achieved due to delays in the main RAM circuitry.
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Sadly the southbridge hosts some truly sluggish performers, for even main memory is blazing fast compared to hard drives. Keeping with the office analogy, waiting for a hard drive seek is like leaving the building to roam the earth for one year and three months. This is why so many workloads are dominated by disk I/O and why database performance can drive off a cliff once the in-memory buffers are exhausted. It is also why plentiful RAM (for buffering) and fast hard drives are so important for overall system performance.

Simple Webcasts on Evolution and Genes

Webcast from 23andme on human evolution. Continued: What are genes?, What are SNPs? (Single Nucleotide Polymorphisms), Where do your genes come from? and What is phenotype?. These webcasts provide an easy to understand overview. Sergey Brin, Google co-founder and husband of 23andme co-founder Anne Wojcicki. People have 23 pairs of chromosomes.

What are SNPs?:

For a variation to be considered a SNP, it must occur in at least 1% of the population. SNPs, which make up about 90% of all human genetic variation, occur every 100 to 300 bases along the 3-billion-base human genome.
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SNPs do not cause disease, but they can help determine the likelihood that someone will develop a particular illness. One of the genes associated with Alzheimer’s disease, apolipoprotein E or ApoE, is a good example of how SNPs affect disease development. ApoE contains two SNPs that result in three possible alleles for this gene: E2, E3, and E4. Each allele differs by one DNA base, and the protein product of each gene differs by one amino acid.