Here’s the blurb from the back:

Working from high-tech labs in Canada or remote villages in Africa, epedemiologists travel the world trying to keep us safe from deadly diseases. Learn how these “disease detectives” are coming up with new wayts to fight disease, and find out if you have what it takes to become an epidemiologist, too!

I’d seen that before. What I hadn’t seen, until the books arrived today, was this very nice cover quote from Jonathan M. Samet, MD, Professor and Flora L. Thornton Chair, Director, Institute for Global Health, University of Southern California, Los Angeles (quite the title!):

“This book captures the excitement and significance of epidemiology and the hard work of being an epidemiologist. It is a great starting point for those who want to benefit world health by becoming an epidemiologist.”

Very nice. And now that I have my author’s copies, I can get the book entered into the Saskatchewan Book Awards before the first deadline of July 31.

In the movie Outbreak, researchers from the U.S. Army Medical Research Institute for Infectious Diseases and the Centers for Disease Control and Prevention (CDC) have to figure out how to stop a kind of super-Ebola virus from ravaging the U.S.

In 1995, the same year Outbreak came out, Marta Guerra, who already had her Doctor of Veterinary Medicine degree and was finishing her master’s degree in public health. “I remember seeing that movie and thinking, ‘Wow, that’s what I want to do!’”

Five years later, Guerra, now with a Ph.D. in epidemiology and a brand-new officer of the Epidemic Intelligence Service (EIS) of the CDC, received orders to head out on her first international mission: to study and combat an outbreak in Uganda of Ebola virus.

She joined a team in Lacor, Uganda, working in a laboratory set up at St. Mary’s Hospital, a private hospital which had much more advanced equipment than the local government-run hospital-equipment that allowed them to diagnose people with Ebola in less than 24 hours.

Accurate, fast diagnosis was important not only to identify Ebola patients but also to allow those who didn’t have the disease to be sent home, minimizing their risk of exposure.

Guerra’s tasks included tracking the spread of the disease, identifying those who had been exposed, and educating the public.

Every day team members would go to the government-run hospital and get the list of people who were newly admitted and/or diagnosed. “Then we would go out to the person’s home and make a list of contacts. Those contacts would have to be visited for 21 days. If there was anyone that appeared to be unhealthy or developing any kind of symptoms, then we would call in to the hospital to bring an ambulance out.” The effort involved 150 trained volunteers.

Over the course of the outbreak, 5,600 people who had been in contact with infected patients were identified and observed.

Although the Ugandan government had done a “wonderful job” educating people about HIV, Guerra says, the lessons learned regarding HIV actually made it harder to deal with Ebola. Whereas people who test positive for HIV remain infected for life, people who recover from Ebola are no longer contagious and are also protected from the disease in the future.

Because of what they’d learned about HIV, people thought that anyone with antibodies to Ebola was contagious and was dangerous to have around. “We’d find [survivors] totally by themselves, without food, because nobody wanted to share their food with them, allow them to use any dishes, anything.”

To counteract that, “I would definitely go up and touch them, or try to show them that I was not scared to be around them. I always ended up giving them some money so they could get food at least for a week to be able to buy some pots and pans and some things to sleep on, because some of them were not being allowed to come back to their families.”

Most people in the area would first be cared for by family members when they became ill, Guerra explains. After that, they would probably turn to traditional healers rather than trying to get to a probably distant clinic. That led to tragedies like the death of all four sisters in one family.

Team members urged the locals to go to a clinic as soon as they felt ill. “If it was malaria, then good, you got your treatment early,” Guerra would tell them, while if it was Ebola, the patient could quickly be isolated and supportive care begun.

The researchers, who knew what precautions to take, weren’t particularly concerned about contracting the disease. They were more worried about the risk of violence.

 ”We were in the territory of the Lord’s Resistance Army in northern Uganda. A lot of people had been displaced up there, a lot of people kidnapped and killed.”

In the end, though, Guerra’s team saw nothing of the rebels. “I think they were also very scared of the Ebola outbreak,” she says, and with good reason: it killed 225 people before being brought under control

Despite the difficulties and dangers, Guerra calls the experience “just wonderful.”

“There are still chances to go out there and help people who are in a very disadvantaged state,” she says. “There are still chances to go out there and do investigations.”

As I’ve noted before, the very first science column I wrote, ca. 1991, was entitled, “What is a scientist?”

Last year I re-ran that column with minor editing: the answer to the question hadn’t changed in 17 years.

But it may have changed now.

That’s because researchers at Cornell University have created a computer program that can derive fundamental physical laws from raw observational data.

In other words, they’ve created an artificial scientist.

By observing the behavior of a single pendulum, a double pendulum, and a spring-loaded linear oscillator (things you might use in a high school physics classroom), their software figured out some basic laws of physics, previously discovered by Isaac Newton and successors.

Big difference: it took human scientists centuries. The computer did it in a few hours.

This research arose from previous work by Hod Lipson, a professor of mechanical engineering at Cornell, on a self-repairing robot called Starfish.

Starfish knew how to repair itself because it was able to create a mathematical representation of the ways in which its components worked together over time. Technically, that’s called a “dynamical model,” but you could—and Lipson does—also call it a “self-image.”

With that self-image, it could make predictions about itself, and use those predictions to detect and repair damage.

Lipson and doctoral student Michael Schmidt realized that if the robot could create dynamical models from data about itself, it should also be able to create dynamical models from data about the surrounding world.

The only difference: whereas Starfish created a dynamical model of itself using robot pieces, the new algorithm creates models from mathematical pieces: variables, operators, symbols, functions.

In their experiment, the results of which were reported in the journal Science on April 3, Lipson and Schmidt fed motion-capture data of pendulums and oscillators into the algorithm. The algorithm started with a huge set of mathematical building blocks it could combine in various ways to recreate the patterns it discovered in the data. Through a process called symbolic regression (inspired by biological evolution), it compared the various combinations against each other, searching for the ones that were invariant—that didn’t change from one observation to the next.

Lipson uses the pendulum as an example. “When you look at a pendulum…some things go up, some go down,” he says. “But to recognize when something goes up another specific thing always goes down to keep the total sum constant, this is a key to understanding the observations in a deeper sense—such as recognizing the laws of conservation.”

The computer hung on to the mathematical expressions that remained constant and dumped those that weren’t. That left it with expressions that both matched the data set and could predict future behavior, which were further tested to ensure they were meaningful and not based on coincidental patterns in the data.

Finally, a human examined the results to figure out exactly what the expressions described. The researchers found that the algorithm, given data on position and velocity over time, discovered energy laws; for the pendulum, the law of conservation of momentum; and given acceleration, Newton’s second law of motion.

Using a parallel computer with 32 processors, analyzing simple linear motion took just a few minutes. The much more complex double pendulum required 30 to 40 hours. However, that time could be reduced to seven or eight hours by seeding the problem with terms from equations already derived for the simple pendulum: in other words, by allowing the algorithm to make use of knowledge it had already acquired, just like human scientists do.

Lipson and Schmidt want to continue their research by using their algorithm to examine biological systems, which are notoriously difficult to model. They hope it will be able to find invariant mathematical processes in the enormous sets of data researchers collect about biological systems and thus reveal previously unknown fundamental laws.

They’re quick to point out that their work doesn’t mean computers will make scientists obsolete. “A human still needs to pick the appropriate building-blocks and framework, as well as give words and interpretation to laws found by the computer,” says Schmidt.

To which a science fiction writer such as myself can’t help but reply, “For now.”

At least, in 2001 the University of California, Berkeley, professor received one of the $500,000 “genius” grants given annually by The John D. and Catherine T. MacArthur Foundation to creative individuals “who provide the imagination and fresh ideas that can improve people’s lives and bring about movement on important issues.”

That’s one way you know he’s a genius. The other is that he has just answered a question that has bedeviled human beings since the dawn of time: “Why are flies so hard to swat?”

Dickinson has built his entire career around the study of the flying abilities of insects in general and flies in particular.

“Flies are the most accomplished fliers on the planet in terms of aerodynamics,” he says. “They can do things no other animal can, like land on a ceiling or inclined surface. And they are especially deft at takeoffs and landings–their skill far exceed that of any other insect or bird.”

How do you study insect flight? Multiple ways.

For instance, Dickinson sometimes puts flies in a virtual reality “flight arena.” He ties them down and then records their wing motions as they react to visual or mechanical stimuli, or even various food odors.

He has also constructed ten-inch-long models of fruit fly wings and immersed them in mineral oil so he could study the currents and vortices set up by their wing motion.

As good experimentation is wont to do, his work occasionally overturns long-standing misconceptions.

For example: to turn, a flying creature has to generate enough torque to offset the inertia of its own body and the friction of the air. Scientists had long assumed that the latter was most important for small insects, for whom the air, they figured, must feel like syrup, whereas inertia was more important in larger creatures like birds.

Dickinson discovered otherwise: inertia turns out to be the most important force flies have to overturn, as well, and this contributes to their amazing turning ability.

Fruit flies make subtle changes in the tilt of their wings relative to the ground and the size of each wing flap to turn, then have to create an opposite twisting force to stop the turn so they don’t spin out of control. If the viscosity of the air were the main force they had to overcome, there’d be no need for that opposite twisting force: the air itself would stop the turn.

More recently, Dickinson combined robotic modeling with slow-motion video to at last answer the question of how honeybees, heavy insects with short wing beats, generate enough lift to fly, in apparent defiance of the calculations of aeronautical engineers.

Dickinson found that bees have an incredibly complex wing beat. The wing sweeps back in a ninety-degree arc, then flips over as it turns, all this happening astonishing 230 times a second. Like the rotation of a propeller, this generates more lift than the ordinary wing beats of larger insects.

Now, another triumph for Dickinson: the answer to the fly-swatting conundrum. High-resolution, high-speed digital imaging of fruit flies faced with a looming swatter (actually a 14-centimeter-diameter black disk, dropping at a 50-degree angle toward a fly standing at the center of small platform) show that no matter what position a fly is in when the threat appears–grooming, feeding, walking, or what have you–the appearance of the disk from any direction causes the fly to quickly–within about 100 milliseconds–adjust its legs into the optimal position to allow it to hop out of the way in the opposite direction.

It’s an amazingly quick and amazingly sophisticated transformation of sensory information to motor response, and it’s saved the lives of innumerable flies over the centuries.

Practical benefits from Dickenson’s research into insects’ aerial abilities include the possibilities of tiny flying robots for surveillance or search-and-rescue use, improved propellers, or more stable aircraft.

But here’s some advice you can use here and now: “It is best,” says Dickinson, “not to swat at the fly’s starting position, but rather to aim a bit forward of that to anticipate where the fly is going to jump when it first sees your swatter.”

See? Told you he was a genius.

I’ll let him explain his research:

Obviously someone I should have contacted when I set out to write my book Genetics Demystified…

Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias.

The Wall Street Journal has an article.

Disturbing if true, especially (to be ridiculously self-centered about it) considering how much I write about research findings in my science column and on this blog!

Tenth on the list this year: whale-feces researcher. Rosalind Rolland, a senior researcher at the New England Aquarium in Boston, combs the Bay of Fundy looking for brown stains deposited by endangered North Atlantic right whales. Rolland can analyze the feces for pregnancy, hormones and biotoxins and examine the whales’ genetics. Who can poo-poo that?

Number nine: forensic entomologist, the folks who estimate how long a corpse has been dead by charting the life stages of the blowfly, for whom a corpse is an all-you-can-eat-and-lay-your-eggs-in buffet.

Buzzing along, we come to Olympic drug tester in eighth. At first blush, that may not seem all that awful, but blush again: as Popular Science puts it, “at the 2008 Beijing Games, dozens of officers at doping-control stations will watch jocks urinate into cups about 4,000 times over 21 days.” If they finger someone, millions of people hate them. If they miss someone, they’re berated as incompetent. No matter how you look at it, urine trouble.

Number seven is “gravity research subject.” Parents of small children see gravity research all the time, usually followed by bruises, band-aids and tears. But these gravity research subjects spend weeks in bed, heads titled down at a 6-degree angle, to mimic the restricted muscle use and increased blood flow to the head experienced by astronauts in orbit. For this, they earn $6,000–even though many of those aforementioned parents of small children might very well volunteer to spin 21 days in bed for free.

The sixth-worst job Popular Science lists is “Microsoft security grunt,” the people who toil to fix problems with Windows, Internet Explorer, Office and all the other Microsoft products. Says Popular Science, “It’s tedious work. Each product can have multiple versions in multiple languages, and each needs its own repairs (by one estimate, Explorer alone has 300 different configurations.)” If they haven’t already, these guys might want to check out the ancient Greek myth of Sisyphus.

Number five is “coursework carcass preparer.” My high school had a whole room in back of the lab filled with jars of pickled critters, none of which were ever taken out that I can recall, which makes me think they’re still lurking in a school lab somewhere. Nevertheless, some schools must use them, because there are whole teams of people who poison, preserve and bag those things. “Most of us enjoy the work,” says one coursework carcass preparer. Hey, free formaldehyde fumes! What’s not to love?

Fourth-worst this year is “garbologist” (someone who digs through garbage for scientific purposes as opposed to, say, fun), while number three is “elephant vasectomist,” an occupation that involves a dart gunner in a helicopter, a crane truck, a specially designed four-foot-long fiber-optic laparoscope attached to a video monitor, and a very long pair of scissors.

The second-worst job on the list is oceanographer, because it’s “nothing but bad news, day in and day out.” If we’re not careful, say oceanographers, humans could remake the ocean into an environment that requires a toxic-containment suit….

…in which case, recreational diving will rank right up there with the worst science job for 2007, “hazmat diver.” Hazmat divers wear fully-encapsulating drysuits, and swim inside nuclear reactors, through toxic spills of all descriptions, and even in…

Well, let Steven M. Barskiy, author of Diving in High-Risk Environments, describe it: “The worst was at a factory pig farm. A guy had driven his truck into the waste lagoon and drowned. Not only was it full of urine and liquid pig feces, the farmer had dumped all the needles used to inject the pigs with antibiotics and hormones in there.” A hazmat diver had to go in and retrieve the body.

So the next time you think your job resembles swimming in a sewer because of all the crap that comes your way, remember–for some people, swimming in a sewer is their job.

You’re not so bad off after all, are you?

Brian Wansink, John S. Dyson Professor of Marketing and of Applied Economics at Cornell, says there’s a lot of truth to that old joke—and he’s done a lot of studies to prove it. (He’s also the author of a book, Mindless Eating: Why We Eat More Than We Think.)

For example, during this year’s Super Bowl he and postdoctoral researcher Collin R. Payne examined the eating habits of 50 graduate students at a sports bar with an open buffet featuring chicken wings. They discovered that those eating at tables where leftover bones accumulated ate 27 percent fewer wings than those eating at tables where leftovers were removed.

A couple of years ago, Wansink led a research team led by Wansink at the University of Illinois at Urbana-Champaign that served a free soup lunch to 54 adults. Half ate from normal 18-ounce soup bowls, while the others ate from identical bowls that were being slowly refilled through hidden tubing.

Those whose bowls were being refilled ate 73 percent more soup, consuming on average 113 more calories over 20 minutes—but were convinced they had eaten no more than those who had normal bowls.

“People use their eyes to count calories and not their stomachs,” is how Wansink put it.

In 2005 Wansink and associates gave 40 university female staff and faculty members at the University of Illinois-Champaign 30 Hershey Kisses in either clear or opaque candy jars, on their desks or six feet away. Each night, they counted the candies, then refilled the jars.

They found the women ate an average of 7.7 Kissses each day when the chocolates were in clear containers on their desks, but only 4.6 daily when they were in opaque containers on the desk. They ate an average of 5.6 daily when the Kisses were in clear jars six feet away, but only 3.1 when they were in opaque jars six feet away.

Interestingly, though the participants ate fewer candies when they were six feet away, they thought they were eating more–which suggests that when you have to expend more effort to get a treat, you’re more likely to think about what you’re eating.

In another Wansink study, adults offered six colored flavors of jellybeans mixed together in the same bowl ate 69 percent more than when the colors were each placed in separate bowls. In yet another, moviegoers given M&Ms in 10 colors ate 43 percent more than those offered the same number of M&Ms in seven colors.

Apparently, we like variety, and the more variety we see, in a candy dish or (worse) at an all-you-can-eat buffet table, the more we eat.

And then there’s the study that showed that when moviegoers were served stale popcorn in big buckets, they ate 34 percent more of it than moviegoers served stale popcorn in medium-sized buckets. When the popcorn was fresh, the difference was even greater: those with large tubs ate 45 percent more than those with medium-sized containers—even though, when asked if they ate more because of the size of the container, 77 percent of those given the large tubs said they would have eaten the same amount even if given the smaller tubs.

Sharks have been called “mindless eating machines”—but apparently, sharks aren’t alone.
Yet another Wansink-led study found that people estimate they make, on average, about 15 food- or beverage-related decisions each day. But in fact, they make more than 15 times that many—an average of 221, in the study.

All of this is contributing to a too-high intake of calories, which leads to insidious weight-gain, which can eventually lead to health problems.

Fortunately, Wansink has some suggestions: use smaller bowls, empty out snacks rather than eating them from a package (yes, he’s done a study that shows people eat less that way), always sit at least an arm’s length away from a buffet table or snack bowl, and keep tempting treats in the back of the cupboard or refrigerator, wrapped in aluminum foil.

In other words, deep-six the see-food diet in favor of “out of sight, out of mind”—and out of tummy.

OK, maybe not that easy…

I can’t think of anyone who deserves a free trip more.