The message is loud, clear, and has reached cultural saturation: women are underrepresented at the top of highly-competitive professions because they cannot reconcile the amount of time needed for such careers with the time they want to spend raising children. Just acknowledging this point has been a recent watershed moment for feminism, triggered by Anne-Marie Slaughter’s controversial Atlantic article and the launch of Sheryl Sandberg’s Lean In. Slaughter and Sandberg offer different views on exactly what’s holding women back, but both agree that much of it has to do with raising children. And, of course, each woman and critic has proposed an array of internal and structural changes to help improve work-life balance for women in highly competitive fields.

Last month, the discussion continued here at The Rockefeller University, where Slaughter presented her thoughts on “The Coming Work-Family Revolution.” Since then, and in light of the larger public debate, young women at Rockefeller have been pondering our own experiences and – let’s be honest – concerns about pursuing a career in academic science.

Academic science offers a special case of the more general problem of women’s underrepresentation at the top of highly competitive careers. This is because the problem we face as women in science is actually with one specific career stage: the postdoc. Survey data show that women mostly opt out of academic science just before or during postdoctoral training. Not coincidentally, this is exactly when they have children.

Given that the average age of PhD awardees is 31, women postdocs fall somewhere on the sharply declining portion of the female fertility curve. This is a crucial biological difference between women and men that may partly explain the skew in departures from academic science. Men can put off having children until they land a tenure track job with relatively few reproductive consequences, but if a woman knows she wants to have a family, the wise bet on her own fertility is on having at least the first child before that point, as a postdoc or earlier. Thus, many women postdocs have children, which dramatically increases the odds of their departure from the road to being a PI.

However, the connection between having kids and women leaving science is not simply a matter of long-term work-family time balance. Rather, it is specific to the postdoctoral stage, despite both graduate school and pre-tenured professorship being incredibly demanding. Once women get permanent academic jobs, they leave at a much lower rate, even though their children are presumably still around. This suggests that there is something about being a postdoc in particular that motivates women to leave.

Most postdocs are only paid around $45,000 per year. This is dramatically lower than the compensation of other professionals with similar levels of experience and education. Such low pay leaves women postdocs with much less money than other professional women to pay for childcare and stopgap measures like sending the laundry out. The compensation gap during the postdoc persists for years at the middle of their career, when saving is most important for long-term financial wealth. There is therefore a huge opportunity cost to staying in academic science given the alternatives available to smart, motivated, highly trained postdocs. Adding on the costs of children in both time and money may make other opportunities impossible to turn down.

Women postdocs’ departure speaks for itself. Women leave science to become industry researchers, teachers, consultants, editors, journalists, lawyers, and so on. Many of these professions lack female role models at the top. Most have less flexible hours than science. All of them require hard work. But, again and again, women choose these options over continuing in academic science. We have to ask ourselves why, and the answer is obvious—money. These options all differ from academic science in one key aspect: they are better compensated and more secure at precisely the stage when women opt out.

Of course, many male postdocs also have children. So why don’t they leave at the same rate as women? They soon may. The current makeup of the top ranks of academic science reflects previous generations of scientists. Men’s role in parenting and expectations of fathers are changing so rapidly that the advice Slaughter and Sandberg offer women about making sure to find a partner who will help you balance career and family seems almost quaint. Going forward, the feeling of being torn between career and family may be just as strong for fathers, and the postdoc brain drain may comprise devoted parents of both sexes.

To change the economic calculus for potential scientists, we could pay postdocs more. Imagine that we paid postdocs, say, $75,000/year. This is a modest increase from current rates, but for individual postdocs, and the structure of academic science, it would be transformative. It would allow postdocs to actually afford childcare. They might be able to have a spare bedroom in their apartment for family members to stay in when helping to care for children. They might even be able to put some money away to buy a house or save for their children’s education. These types of changes certainly wouldn’t catapult postdocs into the realm of the rich, but they would make all the difference to a 32-year-old woman who wants children and is considering her next career move.

How could we accomplish this? A major granting agency like the NIH or HHMI could change its salary guidelines, and we could pay for it by having fewer postdocs. Making postdocs more expensive to employ – and having fewer of them – would have the added bonus of decreasing the applicant pool for assistant professorships. The dismal odds of being able to get a faculty job in academic science are certainly not incentivizing women to stay, and weeding people out earlier would decrease the cost sunk into postdoctoral training for those who ultimately don’t get jobs. Of course, we could also remove the postdoc bottleneck entirely by actually increasing overall funding for science to create more highly-paid tenure-track PI positions.

In the end, being an academic scientist, even a tenured professor at a top institution, should not be incompatible with actively raising children. So, let’s do an experiment. See what happens to women’s choices if a large cohort of postdocs can count on being paid a more secure family-friendly wage and not losing out when they spend time with their children. We owe it to the public that ultimately pay for science to make sure the people most likely to make groundbreaking advances become scientists, whether or not they also want children.

Taken from The Daily Doodle.

My name is Maryam and i’m a congenital anosmic. I was born this way—a rare mutant with a lifelong inability to smell

Anosmia literally means ‘without smell. While I most certainly do have a nose (my grandmother would even say it’s impressively large), it is incapable of telling my brain that it is sensing anything. When a person smells, what the nose is actually detecting is a series of tiny odor molecules in the air, which make their way through the nasal passage. There, they bind to odorant receptors located on the surface of olfactory neurons. These receptors recognize a given odor molecule by its characteristic shape and size. Once a particular receptor binds and recognizes an odor, it initiates a series of changes in the neuron. This neuron then *fires* a chemical message, setting off a chain of events—a signaling cascade—that relays the presence of that particular smell up to the brain.

The average person can bind and distinguish up to 10,000 different odor molecules, which is a whole heck of a lot considering humans have a relatively poor sense of smell! I, on the other hand, have a genetic mutation—a typo in my olfactory neurons’ assembly instructions—that leaves me unable to detect a single scent. While it’s likely that my nose’s odorant receptors can still recognize and bind odors*, this smelly message gets lost because some link in the signaling chain to my brain is defunct.

Unfortunate as that may sound, as a New Yorker, I consider my deficiency more of a blessing than a curse. I shrug obliviously as my friends complain that a Bushwick street corner smells like pee. I don’t faint when sparky the dog passes gas in a closely quartered lower east side apartment. I even get the last seat on the train that no one wants just because it’s next to some [allegedly] super smelly person.

Of course, there are downsides too. For instance, one night, some friends and I were riding the subway home from dinner. The train that came was beyond crowded except for one car. I marched into that car, happy as a clam to find a seat—nay, a whole bench!—for myself. That is, until my friends followed me in and started choking on the stench. At first I panicked, thinking the smell was me [my friends are constantly assuring me that their p-u's are never for me]. But then I saw the lone man in the car throwing up in his jacket. If not for my friends’ good scents, I would have unknowingly lounged in the smell of vomit for the next 20 minutes.

Fortunately, this is one study I will never qualify for!

The biggest downside, however, is that my nose is deaf to the inaudible // invisible // intangible language of odors. Animals silently communicate with one another through the smells they give off, and humans are no exception. Our brains have the capacity to translate olfactory stimuli into a behavioral response. We transmit emotion through scent—the stink of fear is contagious. We recognize our kin through their signature smell—infants sleep better with just the scent of their mothers nearby. And, most famously, we choose our mates by their fragrance. In a blind study, ovulating women preferred the scent of more symmetrical men based only on the way their slept-in t-shirts smelled. On the flip side, researchers have found that men tip strippers better while the strippers are ovulating!

Anosmics like myself are thought to be indifferent to these behavior-inducing odors. We’ve been accused of being more socially awkward and less confident than the average smelling human because we cannot pick up on these intangible olfactory cues. Reduced scent perception has even been implicated as a marker for psychopathy! As a non-smelling, well-functioning [albeit super nerdy] individual, I wonder if that’s truly the case. If a blind // deaf person makes up for the loss of one sense by heightening the others, who is to say that my other senses aren’t compensating in a similar way? That maybe ansomics make up for what we can’t smell by being hypersensitive to a person’s tonal inflections or slight changes in facial + body language?

Anosmics aren’t inscentsitve… we just have a different way of smelling the world. Perhaps there is even a way to reveal the hidden world of scent to the unsmelling. A deaf person can ‘hear’ music by feeling its rhythms and melodies. Scientists have found ways to enable a blind person to ‘see’ by translating images into sound waves. Perhaps, then, there is still hope for the inscentient. A way to manipulate the anosmic’s brain—mimicking a smell to evoke a response—to give us a whiff how smell looks // feels // tastes // sounds.

The Scent of Light. Shanghai’s super nature design attempts to evoke scent using ethereal light design.

* Humans have 900+ genes coding for smell receptors, so it’s highly unlikely that every one of these genes is defective in me.

This article first appeared on ArtLab.

Obama announcing the BRAIN initiative

In February, the media was abuzz in response to President Obama’s pledge to fund the creation of a Brain Activity Map (BAM). Reporters erroneously promised a comprehensive human brain map, complete with cures for neuropsychiatric disorders, while glossing over the fact that BAM was actually more about technology development using model organisms. I joined a growing number of commentators on the project, presenting arguments for why this seemed like a 3 Billion Dollar Mistake.

On April 2, 2013, the White House formalized the proposal, rebranding it as the BRAIN Initiative (a tortured backronym for Brain Research through Advancing Innovative Neurotechnologies; #BRAINI). Here, I’ll break down the main aims of the project along with the strengths and weaknesses of the BRAIN initiative and the associated PR campaign.

The Goal

The White House announcement summarizes the BRAIN initiative as follows:

The BRAIN Initiative will accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought.

The Funding

Government:

$40 million in the first year: National Institutes of Health (NIH), via the existing NIH Blueprint for Neuroscience Research, which also includes the Human Connectome Project.

$50 million in the first year: Defense Advanced Research Projects Agency (DARPA)

+     $20 million in the first year: National Science Foundation (NSF)

$120 million total in the first year

Private

$60 million annually: Allen Institute for Brain Science

$30 million annually: Howard Hughes Medical Institute (HHMI)

$4 million annually: Kavli Foundation

+     $28 million over 10 years: Salk Institute for Biological Studies

~$100 million per year

If the government manages to scrounge up $100 million every year (so far they’ve only committed to one) and the BRAIN initiative lasts for the proposed 10 years, the total funds for the initiative could reach $2 billion. That’s less than the $3 billion initially promised, but it’s still sizeable. Regardless, the BRAIN initiative is a big investment.

The Labs

Investigators at the private research institutes listed above are surely going to be working on BRAIN initiative-funded projects, but researchers in other institutions will get a shot at the funds too. Government derived (and possibly some private) BRAIN initiative funds will be awarded to labs that can make a case for their research. Luckily, there’s a team of scientists in charge of choosing which labs are worthy, which brings us to…

The Scientific Advisors

The NIH has assembled an all-star team of neuroscientists to chart the course for the BRAIN initiative and take it from a nebulous “initiative” to an actual research enterprise. This Advisory Committee to the NIH Director will articulate the goals of the BRAIN initiative and develop a scientific plan. The committee’s first report on high-priority investments is due in fall 2013, and their final plan will be presented to the NIH in June 2014.

The Good:

The NIH Advisory Committee is one of the best features of the BRAIN initiative. The co-chairs of the committee are Cornelia “Cori” Bargmann, a highly acclaimed researcher from The Rockefeller University who studies the nervous system of C. elegans worms; and Bill Newsome, a professor at Stanford who studies the primate visual system. I cited Bargmann in my initial post as a voice of reason in the world of “brain mappers.” She has been critical of large-scale neuroscience initiatives in the past, and will help guide the BRAIN initiative with care and scientific rigor.

The real mission is clear; It’s technology development. The initial BAM announcements focused on brain “mapping” with little attention paid to how scientists were supposed to actually accomplish those goals. Now, thanks to the recent White House announcement, the true focus of the project has been put right in the title (remember that the “AIN” of the BRAIN initiative stands for “Advancing Innovative Neurotechnologies”). This will hopefully dissuade hype-happy journalists from presenting the BRAIN initiative as a straight-to-application project.

The BRAIN initiative funding won’t subtract from the pool of general grant money, as confirmed by Francis Collins, the head of the NIH, in a live answer session on the White House blog.

There is an emphasis on public input and open access. The BRAIN initiative results will be open to all researchers. Whether or not they’ll be behind a paywall, or written for a general audience, remains to be seen. The NIH statement also claims that the advisory committee will “seek input broadly from the scientific community, patient advocates, and the general public.” This is welcome news to scientists who have been actively discussing BAM and the BRAIN initiative on blogs and Twitter.

The Not-so-good:

BRAIN initiative coverage in the press is still focused on curing diseases in the human brain. While it’s true that techniques like CLARITY are bringing us a lot closer to large scale mapping of the human brain, most of the BRAIN initiative-funded technologies are going to involve model organisms like fruit flies or mice. These are great systems for technology development, but any conclusions drawn from those animal studies will take years of validation before they can be applied to humans.

Even worse, the BRAIN initiative PR team is selling the initiative by promising cures for neurological diseases. The tools developed using BRAIN initiative funds will be used to increase our understanding of how brains work. Any therapies for brain disorders stemming from this research would be an added bonus, not a primary goal or a benchmark for success. If the BRAIN initiative was designed to develop therapies for schizophrenia and Alzheimer’s, it would be an altogether different initiative, involving genomics, clinical trials, and more targeted studies. That might be worth funding, but it is not the BRAIN initiative, and it is dishonest to claim otherwise.

***

In my opinion, the BRAIN initiative will be a strong source of funding for new neuroscience technologies. However, neuroscience is not the only field of research that deserves such attention, especially in light of wide ranging sequestration cuts. Hopefully policy makers will take the good from the BRAIN initiative – while avoiding the PR pitfalls – to fund wide-ranging scientific endeavors in the future.

Read more:

• The list of BRAIN initiative response pieces grows longer by the day, but Jason Pipkin has done an excellent job of keeping track of them all on his blog. Check out the list here.

• I would especially like to point out the ongoing BRAIN initiative coverage over at Nucleus Ambiguous. Michael Carroll has kept up a steady stream of thoughtful, well-researched pieces since the BRAIN initiative was still BAM and his blog is a great resource.

Follow Gabrielle on Twitter (@GabrielleRab) and browse the Behind the Buzz Archives

 We are biased. When evaluating someone’s competence in a professional setting, we tend to consider other factors beyond qualifications and achievements. One particularly troubling example is gender bias. We still see women as less competent for certain professional tasks, and there is evidence to back up this claim. Smart research done by Corinne Moss-Racusin and colleagues at Yale University elegantly supports the existence of gender bias in science.

The experiment was elegant due to its simplicity. The authors objectively tested faculty from research-intensive institutions for their gender bias. The scientists conducting the study made up a resume and asked 127 faculty scientists to rate the qualifications of the fictional student (the volunteer scientists did not know the student was fictional) who was applying for a research assistant position. A female name (Jennifer) was assigned to half of the faculty participants, and to the other half, a male name (John) was assigned. Otherwise, the CVs were identical. Probably not surprisingly, the male student was rated higher for competence and hireability, even by female faculty. In addition, faculty would be willing to give the male student more mentoring and a higher starting salary.

I wrote “probably not surprisingly” because the gender gap in Science and Engineering (S&E) fields is still alarmingly large, suggesting discrimination against women. By 2010, only 21% of full-time faculty positions were held by female scientists. But correlations do not imply causality, and the gap between male and female scientists in faculty positions has been attributed to factors other than discrimination, including a lack of women who desire advanced scientific positions or who are as equally skilled as male applicants. Harvard’s ex-President Lawrence Summers endorsed both arguments in his remarks at an S&E conference several years ago.

The “lack of skills” argument has been built, in part, upon the fact that over the past 20 years, more male than female high school students have performed at the top 5% in math tests.  The reasons for this difference in performance has been extremely difficult to pin down. One tested hypothesis is that males and females respond differently to testing, and thus math tests results may not be predictors of actual math skills.

So why was ‘Jennifer’ rated less highly than ‘John’ by the Yale faculty who participated in this study? Most scientists consciously strive to be objective. We are trained to consider hard facts, and to move beyond emotions when making judgments and statements.  But research has shown that people who highly value objectivity and fairness are particularly prone to express biases. This probably happens because “objective” people overestimate their invulnerability to hidden bias and fail to monitor its influence on behavior [1, 2].

In this context, I felt a responsibility to test myself in an Implicit Association Test (IAT) for gender-science association. IAT is a computer test that measures the strength of association between classes of concepts (like male-female vs. career-family) by observing response times in categorization tasks. The idea is that it is easier to make a decision when concepts that we think belong together share the same response key. For example, faster responses for the {male career/female family} task compared to the {male family/female career} task indicate a stronger association of male and career over female and career.

IAT is a robust test; it displays internal consistency, independence towards familiarity of the subjects with the method, and resistance to faking (reviewed in ref. 3). More importantly, IAT is predictive of behavior. In socially sensitive topics, IAT is a better predictor than self-reported attitudes [3].

So, I tested myself, and I think that everybody interested in being egalitarian should do it too. I tried two different tests, several times: (1) male, female vs. career, family and (2) male, female vs. science, liberal arts.

I found some variability in my results, which is not unexpected. The average of my tests could be summarized as a very slight association of male with career and female with family, and male with science and female with liberal arts.

Would I have rated Jennifer lower than John in Moss-Racusin’s experiment? Probably yes. As shocking as this might sound, I am not discouraged. Being aware of our hidden biases is the starting point to gaining control over them and to minimize their influence on our behavior. If you obtain an implicit preference you don’t like, well, you might choose to remember that IAT is not perfectly accurate, and move on. Alternatively, you could accept the fact that you might actually show bias and then try to undo it. For example, avoid situations that reinforce the stereotypes.

As a final remark, I would love to read similar research about underrepresented minorities in S&E. African Americans and Hispanics, who represent 30% of the American people, account for only 5% of the work force in S&E. In addition, African Americans are 10 percent less likely to be awarded NIH RO1 funds compared to whites. Could these statistics reflect another implicit bias that we need to acknowledge and fix? I think they do. How about you?

References

1. Monin, B. & Miller, D.T. Moral credentials and the expression of prejudice. J. Pers. Soc. Psychol. 81, 33-43 (2001).
2. Ulmann, E.L. & Coehn, G.L. ‘‘I think it, therefore it’s true’’: Effects of self-perceived objectivity on hiring discrimination. Organ. Behav. Hum. Decis. Process. 104, 207–223 (2001).
3. Greenwald, A.G., Poehlman, T.A., Uhlmann, E.L. & Banaji, M.R. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. J Pers Soc Psychol 97, 17-41 (2009).

]]> http://incubator.rockefeller.edu/?feed=rss2&p=1208 0 http://incubator.rockefeller.edu/?p=1201 http://incubator.rockefeller.edu/?p=1201#comments Tue, 23 Apr 2013 03:04:03 +0000 Jeanne Garbarino http://incubator.rockefeller.edu/?p=1201 By Carol Feltes

We have already learned how to reach out on the worldwide web to engage the online community in joint projects and problem solving, but less so when it comes to non-virtual communities.  Citizen Science is a delightful recipe to lure unsuspecting non-scientists into engagement with science and our physical world.

It is a book with a gentle approach that can appeal to all ages, and acknowledges every effort or contribution, no matter how small, has real value.  It is a book that science teachers and local government agencies, such as parks departments, should use as resource and a model.  There are plenty of great ideas for K-12 science projects, supplemental activities to classroom instruction, and it creates a foundation for lifelong interest in science and an understanding of the environment.  It is rife with ideas that communities, scout troops, families and even seniors’ groups can use to create shared experiences and real, useful, scientific data.

This is “crowdsourcing”  in a delightful “analog” way!  The instructions are all here.  Get your friends together and go for a walk…make a contest out of observing the world, and life happening, all around you.  Add what you see and learn to the data contributed by so many other observers and how together we see everything much more clearly.

Porkchops with Cider, Horseradish, and Dill.

I am spoiled when it comes to pork chops. My dad is a great cook, and he used to make pork chops for dinner a lot.  When I was twelve or thirteen, my best friend, Melissa, came over for a pork chop dinner.  While she loved to hear Peter Brady talk about them, pork chops were her least favorite meal. But she was polite, so she tried it. She was shocked by my dad’s dinner: “Wait, they aren’t always dry?”

So I was excited last weekend when my boyfriend and I decided to try a recipe for Pork Chops with Cider, Horseradish, and Dill from The Smitten Kitchen Cookbook, by Deb Perelman. I am a huge fan of Smitten Kitchen, pork chops, cider, and horseradish (not so much dill, but oh well), so this sounded good.  I was hoping for some juicy pork chops like my dad used to make.

Our attempt at making dinner had mixed results. On the injury front, I did burn my fingers moving a hot roasting pan in the sink but (for once) didn’t end up with any blisters. While I was moderately successful in not getting injured, I was much less successful in not ruining dinner.  I don’t know if it was the recipe or our technique, but the chops were chewy and dry.

But, hey, dry pork chops are almost the rule, right?  Melissa is evidence that some people don’t even know they can be tender and moist, and maybe you’ve heard that pork needs to be cooked well done. This belief is based on some fun science that is especially dear to my parasitologist heart (in the lab, I study these guys).

People worry about undercooked pork because of a parasitic worm called Trichinella spiralis. T. spiralis infects most meat-eating animals, including us humans; it just happens to be more common in pigs because of the way domestic pigs are fed (you can feed them anything, including garbage). Cooking kills these worms, though, so if there’s a chance your pork is infected with T. spiralis, it makes sense to cook that pork until it’s well done.

Adult Trichinella spiralis.

T. spiralis infection, also called trichinosis or trichinellosis, is not very common anymore, but it can make you sick, with a range of symptoms from stomach pain to problems with coordination or breathing.

Of the infections that do occur only about 5% are fatal, and many cause no symptoms at all. This is one of those cool things about infectious diseases: pathogens have evolved to survive, and they aren’t going to survive too long if their host is dead.  There’s a delicate balance between using host resources and keeping the host alive.

Whether their infection is deadly or has no symptoms, these worms do some incredible things once you’ve eaten them. When a human eats undercooked meat infected with T. spiralis, digestive enzymes in the stomach and small intestine free the worms from the cysts in which they live.  These cysts are actually repurposed muscle cells that scientists call “nurse cells.” Once the worms have broken out of their nurse cells, they mature and then mate in the small intestine, producing larvae who dig their way out of the intestine and into the blood or lymphatic system. Once in the blood or lymph, they can travel around the entire body, entering cells. Invaded cells usually die, except for skeletal muscle cells, where these little guys like to settle down.

By the way, when I say T. spiralis is “little,” I mean that its larvae can fit inside cells.  Relative to those cells, though, they’re pretty big. It’s no surprise they kill off most of the cells they invade. Skeletal muscle cells are some of the biggest cells in your body, but T. spiralis has to perform some major contortions (spiralis, get it?) to fit inside.

Nurse cell with T. spiralis squished inside.

I love the sinister way they describe T. spiralis and the nurse cell on Wikipedia: “This nematode is a multi-cellular parasite that lives within a single muscle cell, which it modifies according to its own requirements.”

Turns out, they are pretty sinister. One of the ways a worm modifies a muscle cell “according to its own requirements” is by stimulating the growth of blood vessels around the cell. Not only can the worm hide out from the immune system by hanging out in your cells, but it can also steal route a steady supply of nutrients to itself. On top of this, the larvae don’t like oxygen, so they change the metabolism of the muscle cell.  A cell that was once aerobic, using mitochondria to produce energy from oxygen, turns anaerobic: the worm shuts down all the mitochondria. He curls up and gets cozy in his new home, waiting for the muscle he’s living in to be eaten by a predator or scavenger, starting the whole cycle over. It’s still a mystery to scientists how T. spiralis manages this major muscle cell renovation.

Pigs will eat anything–so they are more likely to pick up Trichinella.

Lucky for those of us in the United States, the odds that you’re going to find any worms in your pork chop if you buy it at a supermarket in the US are extremely low. The US outlawed the feeding of uncooked garbage to pigs in 1954 (yes, really, that’s a law), and the rates of trichinosis dropped precipitously.  Most pork is also frozen before it is sold, so even if a pig picks up T. spiralis while munching on some roasted garbage, freezing his meat will kill any larvae.

So the next time you cook some pork, remember that it doesn’t need to be well done; the USDA recommends cooking pork to 145F, and then letting it rest for 3 minutes. Hopefully it will turn out better than ours did, and no creepy little worms will be taking over your muscles…

Read about Monica’s series, Kitchen Science, here.

Image Credits:

• Pork chops and applesauce, Deb Perelman, The Smitten Kitchen Cookbook
• Trichinella spiralis, http://www.medicine.mcgill.ca/tropmed/txt/
• Nurse Cell, Dr. Dickson Despommier, trichinella.org
• Pig Farm, http://ebeyfarm.blogspot.com/2010/09/pigs-eating-produce.html

//

Oven burns so far… why are they all on my left arm?!

I’ve always argued that baking is a lot like doing experiments: you just follow the recipe. The difference is, when you’re baking the recipe usually works (unless it’s a pie crust).  I’m a grad student here at Rockefeller and when my experiments aren’t working in the lab, I find some consolation in following a protocol that’s going to give me the results I expect.  Of course, it also never hurts to butter up your thesis committee (i.e. the people who decide whether or not you get your PhD) with some of Grandmom’s jewish apple cake.

Although I enjoy baking, I’ve never been much of a cook. I order takeout for dinner. My boyfriend Patrick, on the other hand, knows what he’s doing in the kitchen – he used to work as a cook.   Recently I’ve been using the dinners we make as an opportunity to learn a thing or two. I’m embarrassed to admit that a couple months ago he had to teach me how to cut an avocado.

My nerdy self can’t stop at just learning how to do this stuff, though.  So I’ve been reading up on the science behind the recipes we try.  Why beat egg whites in a copper bowl? How does a cast iron pan work?  Why does everybody always overcook pork!?!?  In Kitchen Science, I’ll detail my adventures in cooking and the science I’m learning along the way.  I am also pretty excited to document my growing collection of oven burns. Apparently clumsiness in the lab translates to the kitchen…

“Does flossing decrease my risk of heart disease?” No, but is it good for you? Yes.

“Does aluminum cause Alzheimers?” Nope.

“Should I start following the Paleo Diet?” Probably not & paleolithic people probably didn’t either.

As scientists, we’re asked these sorts of questions all the time. Although we’re trained to evaluate scientific ideas, it definitely doesn’t take a PhD to judge the latest craze or newest finding. To do it yourself, follow these 5 steps:

 
1. Separate the sales pitch from the science

Almost everyone is trying to sell something. In articles about science, the sales pitch is usually right in the headline. The science is harder to find.

Start by looking for a quote from a scientist. Read the quote but ignore the spin the author put on it. Don’t forget that scientists can have biases too: be skeptical of scientists who don’t acknowledge the limitations of their research and fail to present alternate explanations. Also, check to see who’s funding the research- they might have an agenda too!

In short, read articles carefully and figure out if the claims they make are based on the facts they present.
 

2. Find the data

Some articles will go beyond quotes and actually describe the studies behind their claims. If they don’t, or if you still want to learn more, use Google Scholar to look for the original source. Search with whatever information you have: the names of the scientists, their institution, or the main topic.

You’ll find some articles that are free and easy to read, but many that aren’t. Even if you can’t access or understand an article, you can still find out if the research was published in a reputable journal. Look for the ‘impact factor’ of the journal where an article was published (search for the journal name + “impact factor”). This number is only given to journals that have been around for at least three years and it reflects how often the papers in that journal have been referenced by other scientists. Look for an impact factor of 5 or higher when deciding if a journal is trustworthy.*
 

3. Evaluate the data

Once you find the data, it’s time to evaluate it. Check to see if the scientists behind the original claim, or another group of scientists, have repeated the experiments (and make sure the results were the same each time!). It’s also better if they analyzed hundreds or thousands of people (or monkeys, or cells, or anything else) instead of just two or three. Also keep an eye out for all the differences between two groups in a study, especially with humans. Things like income levels and access to health care can sometimes explain the reported results better than whatever the article is proposing.

Think about it this way: if you were in charge of figuring out the height of the average American male, you would need to measure a bunch of people to get it right. If you only measured a few people, and they happened to be basketball players, you’d be way off.

Also keep an eye out for misleading graphs. Graphs are great for communicating complicated information quickly, but they can also be misleading. Here are a few classic graphical tricks to watch out for:
 

The “put two things with the same trend on one graph” trick:

(source: Businessweek)

This one’s pretty obvious: correlation does not equal causation. It’s exaggerated, but it’s a good example of how putting two things on the same graph doesn’t mean they’re related… even if it looks like they might be.

The “take out data points” trick:

original (left) vs. doctored (right)

Here, we removed the last (very important) column that put the other columns into context. This is a great example of why you should find the original graph from the primary research article whenever you can.

The “zoom in on the axis” trick:

These two graphs have the same information. In the graph on the right, we’re zooming in on the top bit of information, making it seem like the differences between the columns are bigger than they are. Always check the numbers on the left side (y axis), and what those numbers stand for.

Finally, cover up the title, look at the graph and ask yourself what title you would give the data. If you don’t think you can conclude anything from the graph, you should be skeptical of anyone who claims they can. This applies to any kind of information, not just graphs!

 
4. Put the story into context

Now that you know what the study is saying, find out how it fits into the big picture. Make sure there’s more than one paper written about the subject and look for quotes from scientists who weren’t directly involved in the research. Different labs and researchers will often disagree about a scientific topic (which is why you should distrust the phrase “scientists say”). If you’re having trouble finding alternative perspectives, the Wikipedia page for the topic can be a good place to start, especially if it contains a “controversy” or “criticism” section.

 
5. Ask an expert

If after all this research and data analysis you still feel lost, find someone you trust and ask them about it! Is there a science blogger you like? Tweet at them. If you don’t have a favorite yet, ask us. We’d love to help and point you in the right direction toward solid resources. Nothing beats a real discussion (even over Twitter or email!), but you can also check out neutral, non-biased sites like Mayo Clinic.

 
More helpful resources:

• Google has some great tutorials on how to search. They give advice on which terms to choose and how to evaluate the search results.
• The PLoS journal series, especially PLoS Biology, is an open source, peer-reviewed journal series. All the articles are free, and many are written clearly, so read away!
• PubMed is another search engine for science journal articles.
• This guide shows you how to convert obscure technical measurements into simple terms.
• This list of “predatory” journals is a good guide to what not to trust. These journals will publish any research for a hefty price. They have no quality control.
• This list of 10 Questions you should ask to distinguish real from fake science is a great way to decide whether or not to trust a source.
• The UK National Health Service runs an excellent fact-checking website that can help you look behind the headlines.
• Here’s a great article detailing some of the biases and pitfalls in data analysis, especially for small studies.

 
Useful links from the comments (thanks guys!):

• For even more open access journals check out the Directory of Open Access Journals (DOAJ).
• For physics papers, you might have better luck over at arXiv, which archives reprints.  Make sure the article has been “accepted for publication” in a journal, though!
• There’s also a preprint archive for biology papers called PeerJ Preprints.
• Pubpeer.com is a forum where peer-reviewed scientists comment on and critique recent published findings.
• This thorough post helps you tell “a good website from a crap website”.

 
*This threshold is most relevant to biomedical and basic biology journals. Other fields may have fewer citations, but that doesn’t mean that the research is necessarily untrustworthy. Impact factors certainly don’t tell the whole story about a journal’s value. If you want to know more, check out this article that delves into a little more detail.

A mandrill (Mandrillus sphinx) with bright blue skin tone.

If you’re anything like me, you may have wondered why some monkeys have bright blue skin. Even if you don’t pay careful attention to monkey butts, you may still have wondered what makes some people’s eyes blue.

To answer these questions, it is important to first understand how light interacts with living stuff, which we call biological tissue. Light is a collection of little packets of energy, called photons, that whiz through the air.  Photons come in all different colors, and when these colors are all together, we see white light (like sunlight or light from a light bulb). But photons of different colors act differently when they enter biological tissue.

You can think of a photon as a drunk person walking through a forest.  The drunk person enters the forest and walks into a tree, D’oh!, changes direction (scatters) and walks another short distance and into another tree, D’oh!  D’oh! D’oh! D’oh! …. and before you know it the drunk person exits the forest in a random place going a random direction.

When photons enter the skin, it is just like the drunk person walking in the forest.  Photons bounce off collagen fibers (instead of trees) and emerge from the skin at a random place. In optics, we call this diffuse reflection.  A visual representation of diffuse reflection is when a LASER beam hits the surface of milk, giving off a “glow ball” around the LASER spot. (You can also catch my personal demonstration HERE).

Before it gets detected by your eye, a single photon that penetrates a biological tissue (like skin) will randomly scatter within the tissue many times. But it is important to consider that skin is more complex than the forest analogy. Skin is composed of many layers and contains pigments such as melanin, and this affects how photons scatter. This added complexity is the secret to the blue monkey butt.

To understand how photons move through a complex tissue like skin, lets compare a red photon with a blue photon as it hits two tissue layers.  Tissue “A” is on top, and Tissue “B” is underneath.  If layers A and B were both white (like milk), they would reflect red and blue photons equally and you would see both colors.

But in real skin, the top layer (Tissue A) has only a little melanin, while the next layer of skin (Tissue B) can contain a lot of melanin, which is important because melanin absorbs light, “killing” any photons that encounter it!

In the picture above, both the red and blue photons bounce (scatter) 4 times while they are penetrating the skin.  But because the red photons travel a longer distance in between scattering events, they manage to penetrate both layers of skin.  The melanin in Tissue B absorbs the red photon so it can’t leave the skin and reach your eye.

In contrast, the blue photons take very short steps in between their scattering events, and they don’t penetrate very deep.  They do not make it past Tissue A before they emerge and hit your eye.

A monkey’s butt tissue is arranged somewhat like the illustration above so that blue photons are reflected and all of the other photons (like the red ones) are absorbed.  Only the blue light makes it out and gets into our eyes.  This is why we see we see blue monkey butts!

So now you will never look at a monkey’s butt the same way: you will know what makes it blue!  You will even be able to hold your own when this topic comes up on a first date…

For more about photons…

Another demonstration of optical diffusion can be found HERE.  If you want to go way off the mathematical deep end of diffusion theory, equation 15 of THIS PAPER is a great way to start.  Alternately, if you are sick and tired of diffuse light and just want to see me rock out using ballistic laser light, check out this RAP MUSIC VIDEO.

Or if you’d prefer to invoke the Goldilocks Postulate, and want to try something that is just right in the middle: head over to this online biophotonics course.

P.S. Stay tuned for next time’s discussion about imaging cats through a layers of milk, another wonder of light diffusion.

EmpowerMyHealth

helping people make informed decisions in health and illness

In my last post I laid out a problem: medical and health information is complicated and scattered, making it difficult to navigate the medical world. Yet, we’ve been hearing more about “patient involvement” in health care. What does this really mean? Probably different things to different people.

I recently read Shannon Brownlee’s very interesting book Overtreated, which contains useful lists for the navigation process, summarized here:

1.  Find a primary health care provider that you can talk to. This is so important. Find someone you can communicate with and who can coordinate your care. Even if you change insurance, stick with them if you can.

2. Ask questions. It can be really hard to ask questions of your healthcare provider, and the more expert they are, the harder it can be to talk to them. You may need to make a separate appointment to have this conversation, but when it happens, take notes or record the it so you can get all of the important information.  It’s hard to simply remember everything! You may not have to make decisions during your appointment. You might be able to go home and think about it, talk it over with your family, and sleep on it.

Patient questions can be difficult for doctors too.  We’ve been trained to prescribe treatment, and it’s a shift in our role to also be considered as a guide. To get a sense of how things are changing for doctors, read this thoughtful essay from Dr. Carolyn M. Clancy, the director of The Agency for Health Research Quality (AHRQ).

AHRQ is a federal agency within the US Department of Health & Human Services.  Established in 1989, the mission has been to improve quality, safety, and effectiveness of healthcare for all Americans. There is a wealth of information on the AHRQ site, including a list of  Questions To Ask Your Doctor, and a series of patient and clinician videos explaining why it is important to have these conversations.

3.  Ask for patient decision aids. There are many documents and videos of illnesses and treatments available. Here are some links to helpful patient decision aids:

• AHRQ has developed an Effective Healthcare Program (ECP) with a series of Patient Guides
• The Informed Medical Decisions Foundation has been working to advance Shared Decision Making, as it is called, for twenty years. They have developed over 30 patient guides, found here.

There are also many patient advocacy groups that have developed aids for particular disease. Most of these groups are not-for-profits with a mission to help benefit patients and promote research for new treatments. It’s worth asking your healthcare provider about specific patient advocacy groups, as well as doing an internet search to see if there is a national or international agency representing your disease.

4.  Co-ordinate your own care. Keep records of your tests, prescriptions, and treatments. Consider an electronic health record.

5.  If you have a chronic condition, help yourself to stay as well as you can.  Brownlee presents data that supports the notion that hospitalization can make you sicker. Find out the natural history of your disease, what leads to hospitalization, and do what you can to stay well and out of hospital. Join a patient group, perhaps run by your advocacy group, to help you learn how to manage your illness.

Taking these steps can help you get started with navigating the health system.  Whether you have a new diagnosis or have known about your condition, it’s never too late to start asking questions and get more involved in your care.