Saturday, December 28, 2013

Which Sub-Field of Biology Is The Best? (Does It Really Matter?)

Whenever you hear a question in the form of which so-and-so of so-and-so is the best, be prepared to see that the question may be misguided (sometimes it isn't). There is room for debating whether one Olympic sport, such as track & field, is better or harder than another sport, such as swimming -- Carl Lewis or Michael Phelps? These questions can be fun and very informative. I think they are best understood in the context of companion questions, such as "How do we define best?" and "What is the big picture purpose of this 'best'?" Allow me to illustrate.

The field of biology includes many sub-fields. Broadly defined, biology is the study of life or of stuff that interacts with living things. It ranges from sub-fields that study very small things, such as biochemistry and biophysics, to very large things such as psychology and ecology. There is an unspoken pecking order within the fields of biology, likely due to the amount of abstract thinking that is required in that field. Why is it unspoken? Because people usually don't claim such things to be absolutely true, but I have heard many scientists talk about this "pecking order" based on their experience as professionals. At the top of the biology totem pole is biochemistry, and at the bottom would be something like psychology, which some call a "soft science." The irony is that many of the biochemists are so stressed that they need to see psychologists and psychiatrists just to be functional people. Then there is the field of public health. If the goal of biology -- or medical science, to be exact -- is to save lives, then public health researchers have probably saved more lives than all the sub-fields of biology put together. "Who ya gonna call after a disastrous typhoon? Ghost Busters?"

Take-away point #1. If you're deciding which field of biology to study, don't bother yourself too much with which one is the best. Find the one that interests you, that you enjoy, and that you can be good at with some effort. Leave the bickering and posturing for jokes among friends. 

Take-away point #2. Becoming an effective leader -- in business, science, whatever -- requires the ability to respect everyone on the team or in the company. People who do this well are those who get promoted -- I know, because I vote for those type of people. Being ruthless and selfish will get you to the top faster, but being generous and respectful is what keeps you there (or at least helps you sleep better and decreases your risk of unhealthy addictions). I've worked in retail companies, IT companies, educational companies, university laboratories, and national laboratories. I see this again and again... But here are some other interesting questions. Where is this top? And what is success?

Category: Lessons From Science


Tuesday, December 17, 2013

Science Must Learn From The Humanities

The other day I was teaching about the cell cycle and mitosis, when a colleague of mine who teaches history walked in and saw the whiteboard. "Wow, this is so technical," he said, "I just teach history." Assuming that he thought that history was less important than biology, I seized the moment and reminded him of the crucial role of historians. Scientists create technologies that improve life, but historians remind scientists not to destroy the world with that technology -- we call them "whistle blowers." 

The scary thing is that history repeats itself. Nuclear energy is great, but then we now have nuclear weapons. Biotechnology is great, but now we have exquisitely refined chemical and biological warfare. Chemical engineering is great for extracting oil and natural gas from the ground, but unregulated hydrofracking pollutes the environment and causes cancer (1) in those who live nearby. You see, science is just a tool. How humans use science is what makes it great or terrible.  

My recent editorial (2) for Cancer InCytes magazine discusses some lessons we learned from reading the historical accounts of a Nazi concentration camp during World War II. These concentration camps not only killed millions of people, they were places where cruel and unethical medical experiments were done on prisoners (3). 

As a science teacher, it's my job to help my students understand science and to move along the course -- if they so choose later on -- of becoming a scientist. If they don't learn to respect the value of the humanities (i.e. literature, history, sociology, etc.), then they risk becoming scientists who wield science in a very destructive way. 


[1] Karen J. Miller. PREVENTION IS THE CURE. Cancer InCytes 2012, 2(1):e.

[2] David H. Nguyen. CAN MORALITY BE LEGISLATED? Cancer InCytes 2013, 2(2):e. 

[3] Methodist Hospital, Houston (2012, December 6). Survivor of Nazi 'twin experiments' talks to doctors about human subjects research.

Category: Lessons From Science

Friday, December 6, 2013

Tips for Transferring Small Volumes in PCR (or Other) Experiments

This article is for those who are learning how to do PCR. Other articles that describe PCR are in the "Further Reading" section after the text. 

PCR stands for Polymerase Chain Reaction, a technique in biomedical research that is used to make copies of a certain length of DNA. 

PCR, reverse-transcription PCR (RT-PCR), and quantiative PCR (qPCR; qRT-PCR) involve handling sub-microliter volumes of liquids that need to be mixed.  One major source of experimental error is when people re-use one pipette tip for aliquot-ing small volumes of the same reagent, thinking that as long as they avoid contamination with the other reagents their experiment will be fine.  What they don’t realize is that the volume increases with each use of a micropipette tip. Thus, they will see trends in their replicates that should not be there, or the readings between replicates will be very sporadic.  The optimal way to do it is to use a new tip every time.  Also, to minimize user-introduced trends in the data, they should not aliquot a reagent into all replicates for treatment group A, and then to treatment group B.  There is no way to completely get rid of user imprecision, so one way around this is to aliquot sample A1, then B1; A2, then B2; A3, then B3, etc. (See Figure 1). These suggestions would apply to other assays that require distribution of small volumes from the same source. 

Figure 1

Further Reading

Excerpt from "What You Need to Know About the International Test Scores"

What You Need to Know About the International Test Scores
Huffington Post,12/03/2013 3:24 pm

By Diana Ravitch, Historian, NYU Professor

The myth persists that once our nation led the world on international tests, but we have fallen from that exalted position in recent years. 
Wrong, wrong, wrong.
The U.S. has never been first in the world, nor even near the top, on international tests.

In my recent book, Reign of Error, I quote extensively from a brilliant article by Keith Baker, called "Are International Tests Worth Anything?," which was published by Phi Delta Kappan in October 2007. Baker, who worked for many years as a researcher at the U.S. Department of Education, had the ingenious idea to investigate what happened to the 12 nations that took the First International Mathematics test in 1964. He looked at the per capita gross domestic product of those nations and found that "the higher a nation's test score 40 years ago, the worse its economic performance on this measure of national wealth-the opposite of what the Chicken Littles raising the alarm over the poor test scores of U.S. children claimed would happen." He found no relationship between a nation's economic productivity and its test scores. Nor did the test scores bear any relationship to quality of life or democratic institutions. And when it came to creativity, the U.S. "clobbered the world," with more patents per million people than any other nation.
Baker wrote that a certain level of educational achievement may be "a platform for launching national success, but once that platform is reached, other factors become more important than further gains in test scores. Indeed, once the platform is reached, it may be bad policy to pursue further gains in test scores because focusing on the scores diverts attention, effort, and resources away from other factors that are more important determinants of national success." What has mattered most for the economic, cultural, and technological success of the U.S., he says, is a certain "spirit," which he defines as "ambition, inquisitiveness, independence, and perhaps most important, the absence of a fixation on testing and test scores." 
Baker's conclusion was that "standings in the league tables of international tests are worthless."

Tuesday, December 3, 2013

Excerpt from "Policy: Twenty tips for interpreting scientific claims"

This article by Sutherland, Spiegelhalter, and Burgman in the journal Nature is great for understanding the limitations of science and the importance of knowing how to interpret science.


Policy: Twenty tips for interpreting scientific claims
William J. Sutherland, David Spiegelhalter & Mark Burgman
Nature, 20 November 2013

And there is rarely, if ever, a beautifully designed double-blind, randomized, replicated, controlled experiment with a large sample size and unambiguous conclusion that tackles the exact policy issue.

In this context, we suggest that the immediate priority is to improve policy-makers' understanding of the imperfect nature of science. The essential skills are to be able to intelligently interrogate experts and advisers, and to understand the quality, limitations and biases of evidence. We term these interpretive scientific skills. These skills are more accessible than those required to understand the fundamental science itself, and can form part of the broad skill set of most politicians.

Politicians with a healthy scepticism of scientific advocates might simply prefer to arm themselves with this critical set of knowledge.

We are not so naive as to believe that improved policy decisions will automatically follow. We are fully aware that scientific judgement itself is value-laden, and that bias and context are integral to how data are collected and interpreted. What we offer is a simple list of ideas that could help decision-makers to parse how evidence can contribute to a decision, and potentially to avoid undue influence by those with vested interests. The harder part - the social acceptability of different policies - remains in the hands of politicians and the broader political process.

The 20 SSB Tips (Sutherland-Spiegelhalter-Burgman)
1. Differences and chance cause variation.
2. No measurement is exact.
3. Bias is rife.
4. Bigger is usually better for sample size. 
5. Correlation does not imply causation.
6. Regression to the mean can mislead. 
7. Extrapolating beyond the data is risky.
8. Beware the base-rate fallacy.
9. Controls are important.
10. Randomization avoids bias.
11. Seek replication, not pseudoreplication.
12. Scientists are human.
13. Significance is significant.
14. Separate no effect from non-significance.
15. Effect size matters.
16. Study relevance limits generalizations.
17. Feelings influence risk perception. 
18. Dependencies change the risks.
19. Data can be dredged or cherry picked.
20. Extreme measurements may mislead.