Category Archives: Science

Remembering is not enough

teacher
by foundphotoslj
Why is genetics so difficult for students to learn?:
[Via Gobbledygook]

This Sunday morning at the International Congress of Genetics, Tony Griffiths gave an interesting presentation with the above title. He identified 12 possible reasons why students have problems learning genetics. His main argument: students should learn concepts and principles and apply them creatively in novel situations (the research mode). Instead, too many details are often crammed into seminars and textbooks. In other words, students often stay at the lowest level of Bloom’s taxonomy, the remembering of knowledge. The highest level, the creation of new knowledge, is seldom reached, although these skills are of course critical for a successful researcher.

Andrew Moore from EMBO talked about the teaching of genetics in the classroom. He was concerned that a survey found that molecular evolution (or molecular phylogeny) was taught in not more than 30% of European classrooms. He gave some examples of how principles of genetics can be integrated into high school teaching.

Wolfgang Nellen explained his successful Science Bridge project of teaching genetics in the classroom, using biology students as teachers. Interestingly, they have not only taught high school students, but also journalists and – priests (German language link here). Politicians were the only group of people that weren’t interested in his offer of a basic science course.

Teaching is a very specific mode of transferring information, one that has its own paths. It is an attempt to diffuse a lot of information throughout an ad hoc community.

But it is often decoupled from any social networking, usually having just an authority figure disperse data, with little in the way of conversations. There is little analysis and even less synthesis, just Remembering what is required for the next test.

Bloom’s taxonomy is a nice measure of an individual’s progress through learning but it is orthogonal to the learning a community undergoes. Most instruction today is geared towards making the individual attain the highest part of the pyramid.

How does this model change in a world where social networking skills may be more important? What happens to Remembering when Google exists? When information can be so easily retrieved, grading for Remembering seems foolish.

The methods we use to teach at most centers of higher education are, at heart, based on models first developed over a century ago. It may be that they will have to be greatly altered before some of the real potential of online social networks will occur.

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Medicine 2.0

x ray by D’Arcy Norman
Why Health or Medicine 2.0? [ScienceRoll]:
[Via The DNA Network]

While medicine is usually at the forefront of new technology for diagnosis and treatment, the patient-doctor interface has not followed. Perhaps that might change soon.

Some interesting statistics have recently been published. According to Pharma 2.0:

99% of physicians are online for personal or professional purposes
85% of offices have broadband
83% consider the Internet essential to their practice

So doctors are online.

At The Deloitte Center, you will find even more details about the web usage of health consumers. Yes, there will be much more patients who seek health-related information on the web and who want to communicate with their doctors via e-mail or Skype.

And patients are ready.

We have tools to work with:

And we have concepts.

So it will happen because patients and doctors need to have contact. The question is how long will it take?

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Paper discussions

conversations by b_d_solis
Reputation Matters:
[Via The Scholarly Kitchen]

A new (and flawed) study reveals that reputation matters. In fact, it’s core to scientific expression.
[More]

While the study may not be definitive, the ability to have a conversation on it helps tremendously. Research usually does not progress in a straight, ascending line. It switches back and forth, sometimes having to retrace its steps in order to find the right path.

Being able to discuss the results of a paper, what it did right and what it did wrong, is not something that usually has occurred in public. Now it can. I expect there to be more and more such discussions as time goes on.

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Browsing clouds, not papers

Commentary: Summarizing papers as word clouds:
[Via Buried Treasure]

The web provides entirely new avenues for decimating information and for visualizing it. It can be very time consuming to browse throught the literature, even though the most creative research often comes from the intervention of Serendipity (the Wikipedia article lists many examples).

Lars discusses some interesting numbers and comes up with an intriguing solution.

For use in presentations on literature mining, I did a back-of-the-envelope calculation of how much time I would be able to spend on each new biomedical paper that is published. Assuming that all papers were indexed in PubMed (which they are not) and that I could read papers 24 hours per day all year around (which I cannot), the result is that I could allocate approximately 50 seconds per paper. This nicely illustrates the point that no one can keep up with the complete biomedical literature.

When I discovered Wordle, which can turn any text into a beautiful word cloud, I thus wondered if this visualization method would be useful for summarizing a complete paper as a single figure. To test this, I extracted the complete text of three papers that I coauthored in the NAR database issue 2008. Submitting these to Wordle resulted in the three figures below (click for larger versions):


These sorts of rich figures could be very useful in a scientific setting, where being able to rapidly filter a large number of articles is important.

However, he does notice that this approach may not work for all articles, unless there are changes made, either in how the articles are written or in the software that creates the visuals.

…I think a large part of the problem is the splitting of multiwords; for example, “cell cycle” becomes two separate terms “cell” and “cycle”. Another problem is that words from different sections of the paper are mixed, which blurs the messages. These two issues could be solved by 1) detecting multiwords and considering them as single tokens, and 2) sorting the terms according to where in the paper they are mainly used.

And it would be easy to adapt the visuals to scientific needs and then be able to track if they are actually useful in practice.

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Now we have article 2.0

ruby on rails by luisvilla*
I will participate in the Elsevier Article 2.0 Contest:
[Via Gobbledygook]

We have been talking a lot about Web 2.0 approaches for scientific papers. Now Elsevier announced an Article 2.0 Contest:

Demonstrate your best ideas for how scientific research articles should be presented on the web and compete to win great prizes!

The contest runs from September 1st until December 31st. Elsevier will provide 7.500 full text articles in XML format (through a REST API). The contestants that creates the best article presentation (creativity, value-add, ease of use and quality) will win prizes.

This is a very interesting contest, and I plan to participate. I do know enough about programming web pages that I can create something useful in four months. My development platform of choice is Ruby on Rails and Rails has great REST support. I will use the next two months before the contest starts to think about the features I want to implement.

I’m sure that other people are also considering to participate in this contest or would like to make suggestions for features. Please contact me by commenting or via Email or FriendFeed. A great opportunity to not only talk about Science 2.0, but actually do something about it.

While there are not any real rules up yet, this is intriguing. Reformatting a science paper for the Internet. All the information should be there to demonstrate how this new medium can change the way we read articles and disperse information.

We have already seen a little of this in the way journals published by Highwire Press are able to also contain links to papers published more recently, that cite the relevant paper. Take for example this paper by a friend of mine ULBPs, human ligands of the NKG2D receptor, stimulate tumor immunity with enhancement by IL-15.
Scroll to the bottom and there are not only links in the references, which look backwards from the paper, but also citations that look forward, to relevant papers published after this one.

So Elsevier has an interesting idea. Just a couple of hang-ups, as brought out in the comments to Martin’s post. Who owns the application afterwards? What sorts of rights do the creators have? This could be a case where Elsevier only has to pay $2500 but gets the equivalent of hundreds if not thousands of hours of development work done by a large group of people.

This works well for Open Source approaches, since the community ‘owns’ the final result. But in this case, it very likely may be Elsevier that owns everything, making the $2500 a very small price to pay indeed.

This could, in fact, spear an Open Source approach to redefining how papers are presented on the Internet. This is because PLoS presents its papers in downloadable XML format where the same sort of process as Elsevier is attempting could be done by a community for the entire communtiy’s enrichment.

And since all of the PLoS papers are Open Access, instead of the limited number that Elsevier decides to chose, we could get a real view of how this medium could boost the transfer of information for scientific papers.

I wonder what an Open Source approach would look like and how it might differ from a commercial approach?

*I also wonder what the title of the book actually translates to in Japanese?

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Two a day

hard drive platters by oskay
15 human genomes each week:
[Via Eureka! Science News – Popular science news]

The Wellcome Trust Sanger Institute has sequenced the equivalent of 300 human genomes in just over six months. The Institute has just reached the staggering total of 1,000,000,000,000 letters of genetic code that will be read by researchers worldwide, helping them to understand the role of genes in health and disease. Scientists will be able to answer questions unthinkable even a few years ago and human medical genetics will be transformed.
[More]

Some of this is part of the 1000 Genomes Project, an effort to sequence that many human genomes. This will allow us to gain a tremendous amount of insight into just what it is that makes each of us different or the same.

All this PR really states is that they are now capable of sequencing about 45 billion base pairs of DNA a day. They are not directly applying all of that capability to the human genome. While they, or someone, possibly could, the groups involved with 1000 genomes will take a more statistical approach to speed things up and lower costs.

It starts with in depth sequencing of a couple of nuclear families (about 6 people). This will be high resolution sequencing equivalent to 20 passes of the entire genome of each. This level of redundancy will help edit out any sequencing errors from the techniques themselves. All these approaches will help the researchers get a better handle on the most optimal processes to use.

The second step will look at 180 genomes but with only 2 sequencing passes. The high level sequence from the first step will serve as a template for the next 180. The goal here is to be able to rapidly identify sequence variation, not necessarily to make sure every nucleotide is sequenced. It is hoped that the detail learned from step 1 will allow them to be able to infer similar detail here without having to essentially re-sequence the same DNA another 18 times.

Once they have these approaches worked out, and have an idea of the level of genetic variation expected to be seen, they will examine just the cgene oding regions of about 1000 people. This will inform them of how best to proceed to get a more detailed map of an individual’s genome.

This is because the actual differences expected to be found among any two humans’ DNA sequences is expected to be quite low. So they want to identify processes that will highlight these differences as rapidly and effectively as possible.

They were hoping to be sequencing the equivalent of 2 human genomes a day and they are not too far off of that mark. At the end of this study, they will have sequenced and deposited into databases 6 trillion bases (a 6 followed by 12 zeroes). In December 2007, GenBank, the largest American database had a total of 84 billion bases (84 followed by 9 zeroes) that took 25 years to produce.

So this effort will add over 60 times as much DNA sequence to databases as have already been deposited! It plans to to this in only 2 years. The databases, and the tools to examine them, will have to adapt to this huge influx of data.

And, more importantly, the scientists doing the examining will have to appreciate the sheer size of this. It took 13 years to complete the Human Genome Project. Now, 5 years after that project was completed, we can potentially sequence a single human genome in half a day.

The NIH had projected that technology will support sequencing a single human genome in 1 day for under $1000 in 4 years or so. The members of 1000 genomes are hoping to be able to accomplish their work for $30-50,000 per genome. So, the NIH projection may not be too far off.

But what will the databases look like that store and manipulate this huge amount of data? The Sanger Institute is generating 50 Terabytes of data a week, according to the PR.

Maybe I should invest in data storage companies.

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Life scientists at Friendfeed

Life Sciences likes this: Friendfeed:
[Via OpenWetWare]

FriendFeed
I’m going to assume that only those currently using FriendFeed will understand the self reference in the title but if you didn’t that’s OK. Just keep on reading, you’ll get it, eventually.

If you happen to be interested or work in the life sciences area I’d recommend you take a few minutes to read Cameron Neylon‘s great post about FriendFeed and how it’s been embraced by the life sciences community.

I won’t go into the details of how FriendFeed works, but it’s been rapidly gaining momentum as a medium for groups of users to network and discuss each other’s shared content.

FriendFeed’s about page states:

FriendFeed enables you to keep up-to-date on the web pages, photos, videos and music that your friends and family are sharing. It offers a unique way to discover and discuss information among friends

The life sciences community has picked up on this great website like wildfire. A recently created room called The Life Scientists grew in a very short period (a week?) from just a few active online colleagues to a whooping 100+ users.

FriendFeed rooms offer a way to share on-topic content and further discussion via comments. Commenting can be done on any shared items (yours or others). This has proven to be useful for rapid input and idea sharing amongst the room’s users.

Amongst the 100+ users of the Life Scientists room, both Cameron from Science in the Open and Pedro from Public Rambling have found FriendFeed to be useful and explain why it works. Both great reads.

This is the sort of tool that can very rapidly connect researchers, in ways that Twitter or Facebook do not. Not only can links be put up rapidly but comments are there very fast. It allows one to ask questions, post answers. It is a lot like how the Bionet newsgroup, which you can still access, used to be back in the old days (i.e. 1993-95) when Usenet ruled the Internet.

This is the online equivalent of the water cooler where you can run into someone and strike up a conversation that could lead to innovative thinking. Only instead of two people having to occupy the same space at the same time, this approach decouples both, permitting a much wider circle of people to be involved.

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This is important

RNA Tie Club from Alexander Rich

Kevin Kelly — The Technium:
[Via The Technium]

Scenius is like genius, only embedded in a scene rather than in genes. Brian Eno suggested the word to convey the extreme creativity that groups, places or “scenes” can occasionally generate. His actual definition is: “Scenius stands for the intelligence and the intuition of a whole cultural scene. It is the communal form of the concept of the genius.”

Individuals immersed in a productive scenius will blossom and produce their best work. When buoyed by scenius, you act like genius. Your like-minded peers, and the entire environment inspire you.

The geography of scenius is nurtured by several factors:

Mutual appreciation — Risky moves are applauded by the group, subtlety is appreciated, and friendly competition goads the shy. Scenius can be thought of as the best of peer pressure.
Rapid exchange of tools and techniques — As soon as something is invented, it is flaunted and then shared. Ideas flow quickly because they are flowing inside a common language and sensibility.
Network effects of success — When a record is broken, a hit happens, or breakthrough erupts, the success is claimed by the entire scene. This empowers the scene to further success.
Local tolerance for the novelties — The local “outside” does not push back too hard against the transgressions of the scene. The renegades and mavericks are protected by this buffer zone.

Scenius can erupt almost anywhere, and at different scales: in a corner of a company, in a neighborhood, or in an entire region.
[More]

Kevin discusses a specific instance of scenius but the idea is something that needs greater examination. Because innovation, creativity and new insights rarely if ever happen because of a single person in isolation. They happen in a social network made up of the right mix of people to allow innovation to blossom. However, an important aspect, especially today, is that the scene for this genius does not need to occupy the same space. The specific network can be made up of people physically separated.

An example from my set of the woods involves a single man who was able to create a scenius that transcended location. It starts at Cambridge University in England in the mid to late 1950s. Using their superb intellects and their well-connected social network, Watson and Crick were able to discern the structure of the DNA molecule. They published this in 1953.

Now this great discovery was noticed by a pre-eminent physicist, George Gamow, who, to my mind, is one of the great scientists of the 20th century, not only for his own work but for his impact on other scientists. Here is how Wikipedia starts his entry:

George Gamow (pronounced as IPA: [ˈgamof]) (March 4, 1904August 19, 1968) , born Georgiy Antonovich Gamov (Георгий Антонович Гамов), was a Russian Empire-born theoretical physicist and cosmologist. He discovered alpha decay via quantum tunneling and worked on radioactive decay of the atomic nucleus, star formation, stellar nucleosynthesis, big bang nucleosynthesis, nucleocosmogenesis and genetics.

Nice, wide ranging scientific career. Look at his accomplishments (again from Wikipedia):

Gamow produced an important cosmogony paper with his student Ralph Alpher, which was published as “The Origin of Chemical Elements” (Physical Review, April 1, 1948). This paper became known as the Alpher-Bethe-Gamow theory. (Gamow had added the name of Hans Bethe, listed on the article as “H. Bethe, Cornell University, Ithaca, New York” (who had not had any role in the paper) to make a pun on the first three letters of the Greek alphabet, alpha beta gamma.)

The paper outlined how the present levels of hydrogen and helium in the universe (which are thought to make up over 99% of all matter) could be largely explained by reactions that occurred during the “big bang“. This lent theoretical support to the big bang theory, although it did not explain the presence of elements heavier than helium (this was done later by Fred Hoyle).

In the paper, Gamow made an estimate of the strength of residual cosmic microwave background radiation (CMB). He predicted that the afterglow of big bang would have cooled down after billions of years, filling the universe with a radiation five degrees above absolute zero.

Gamow published another paper in the British journal Nature later in 1948, in which he developed equations for the mass and radius of a primordial galaxy (which typically contains about one hundred billion stars, each with a mass comparable with that of the sun).

Astronomers and scientists did not make any effort to detect this background radiation at that time, due to both a lack of interest and the immaturity of microwave observation. Consequently, Gamow’s prediction in support of the big bang was not substantiated until 1964, when Arno Penzias and Robert Wilson made the accidental discovery for which they were awarded the Nobel Prize in physics in 1978. Their work determined that the universe’s background radiation was 2.7 degrees above absolute zero, just 2.3 degrees lower than Gamow’s 1948 prediction.

I have to love any genius who authors a paper that makes such a great pun. Some of the best geniuses are great tricksters (Feynman loved to pick locks or break combination safes.)

But my story is not about Gamow and the big Bang theory. I’ll let this, from Nobelprize.org, discussing the breaking of the genetic code, provide some context for Gamow’s genius, and how he created a scenius that spanned continents:

When the structure of DNA was made known, many scientists were eager to read the message hidden in it. One was the Russian physicist George Gamow. Many researchers are ”lone rangers” but Gamow believed that the best way to move forward was through a joint effort, where scientists from different fields shared their ideas and results. In 1954, he founded the “RNA Tie Club.” Its aim was “to solve the riddle of the RNA structure and to understand how it built proteins.”

The brotherhood consisted of 20 regular members (one for each amino-acid), and four honorary members (one for each nucleotide in nucleic acid). The members all got woolen neckties, with an embroided green-and-yellow helix (idea and design by Gamow).

Among the members were many prominent scientists, eight of whom were or became Nobel Laureates. Such examples are James Watson, who in the club received the code PRO for the amino acid proline, Francis Crick (TYR for tyrosine) and Sydney Brenner (VAL for valine). Brenner was awarded the Nobel Prize in Physiology or Medicine as recently as 2002, for his discoveries concerning genetic regulation of organ development and programmed cell death.

Early Ideas Sprung from the “RNA Tie Club”

The members of the club met twice a year, and in the meantime they wrote each other letters where they put forward speculative new ideas, which were not yet ripe enough to be published in scientific journals.

In 1955 Francis Crick proposed his “Adapter Hypothesis,” which suggested that some (so far unknown) structure carried the amino acids and put them in the order corresponding to the sequence in the nucleic acid strand.

Gamow, on the other hand, used mathematics to establish the number of nucleotides that should be necessary to make up the code for one amino acid. He postulated that a three-letter nucleotide code would be enough to define all 20 amino acids.

Eight out of 20 won Nobel prizes (although there is some humorous ways to look at this that give better clues on how this was accomplished). Not very bad odds. Much like Kelly’s mountain climbers. The scenius attracts, nourishes and sprouts geniuses. But it is the first scientific scenius I am aware of that was not tethered to a single location and some very critical things came up from these interactions. For instance, Crick delineated the 20 amino acids used to make up proteins as an intellectual exercise, written on a pub napkin. He was right.

This group worked a lot to try and figure out how RNA made protein, thus the name RNA Tie Club (Gamow made sure each had an appropriate tie for their amino acid). There were many informal and speculative papers that they wrote to each other (remember that this was a time where biology and genetics were mainly descriptive. Speculation and deductive approaches to biology were not commonly used.) Many of these approaches were flat out wrong. But these errors allowed them to eventually gain some wisdom.

Some of the papers have become parts of biology lore, because the speculations turned out to be correct and led to really important breakthroughs in the field. Here is the most important one, Francis Crick and his Adaptor hypothesis, the paper for the RNA Tie Club that developed tRNA and a degenerate genetic code as a model. On Degenerate Templates and the Adaptor Hypothesis is one of the most famous unpublished papers I know of.

To get some idea of how this all worked, check out Watson’s response to Crick Adaptor paper for the RNA Tie Club. Watson was at CalTech at the time.

Gamow. was here for 4 days – rather exhausting as I do not live on Whiskey. Your TIECLUB note arrived during visit. Am not so pessimistic. Dislike adaptors. We must find RNA structure before we give up and return to viscosity and bird watching.

So, Gamow, who was at George Washington University at the time, was in California visiting one RNA Tie Member when the paper from another member arrived. Pretty interesting network.

So much of the early innovations in molecular biology were driven by the interactions of the RNA Tie club. All because a tricky physicist created a scenius without a specific location. Think what could be accomplished today with such a network using Science 2.0 approaches.

Being able to create and foster such a scenius will be an important part of many organizations.

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Why I hate big conferences

You know the conference you are at is too big when ….:
[Via The Tree of Life]

You know the conference you are at is too big when ….


Now – I confess I was really impressed with how ASM handled this enormous meeting I was just at. If you are going to have a big meeting, ASM does a smashing job. And I can see how such big meetings can have their appeal – the diversity of work and activities relating to Microbiology are amazing. However, big meetings are still not my cup of tea.

So here is my top 10 list of “You know the conference you are is too big when …”. All are based on experiences from this meeting.

1. People communicate within the conference venue by email and cell phones
2. They give you a foldout map showing the locations of all the different venues/activities/
3. Colleagues contact you electronically after your talk rather than in person
4. The lines for food are longer than the lines for security at the airport
5. There are more:
• counters at the registration booth than at the airport ticket area
• meeting staff than scientists at the last conference you attended
• promotional booths than active players in Major League Baseball (OK, we are not quite there with this meeting but we are close)
6. The abstract book weighs more than your laptop computer
7. People use GPS to find their way in the conference center (I wish I had pictures but I saw this happening)
8. The bus/shuttle scheduling system is more complex than the travelling salesman problem
9. You need to plan your own schedule by searching a database
10. You do more walking inside the conference center than outside

I have had to deal with every one of these at big conferences. Many of the points hit one of the big drawbacks from mammoth conferences – they depersonalize the experience.

I find that big conference really lose some of the human network aspects that usually make conferences important. They are so big, with so many presentations that it becomes overwhelming. I have found that there are usually only a few sessions I am really interested in and they are all at the same time <grin>.

What can make it worthwhile is not the size. It is like a college reunion – I can connect with people I already know. That is with 2000, 5000, or 10,000 participants, there is a pretty good chance I can hook up with others. So we go out and talk about how out of control the meeting is or how many T-shirts we have picked up.

But the real purpose, to hear presentations about research, to disperse information, is usually just not as much fun. Again, it is like college classes. Ones with 10 people sustain a much larger and more rapid exchange of information than classes of 500.

Unless I am presenting, I generally stick to more focussed meeting with no more than 500 participants. I feel like I learn more. The speaker is not mobbed afterwards making it easier to talk with him. If the discussion extends beyond the next presentation, we can often continue outside the hall without the need to feel that we have to rush to another session.

Big conferences often give me little reason to attend. Their massive size is disconcerting. It is harder to find a hotel or restaurant. The social interactions are diminished. Why take the effort?

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