Wednesday, September 25, 2013

Hospital Outbreak Management Using Genome Sequencing

Here's a great Q&A with John Rossen, who heads a molecular sequencing department at the University Medical Center in Groningen, Netherlands.  His team is implementing next-gen sequencing for detecting and managing bacterial and viral outbreaks in hospitals.

The general approach in their pilot studies is to isolate the bacteria responsible for the outbreak, sequence the genome, and based on the information produces they determine what the bug is and whether there are any features (like SNPs) it carries that make it unique.

They then develop a quicker, PCR based test capable of detecting the specific strain around the hospital and use it to collect data and make decisions to manage that unique outbreak.

There are still some cases where characterizing drug resistance patterns of the bacteria is involved, involving slower, intentional, experimentation and microbiology lab based detective work.  Eventually, you might be able to dispense with this and predict (with excellent accuracy) what the bugs are resistant to using sequencing.

Nevertheless, it's pretty clear that the genomics approach is paying off for them in terms of speed:
It's quicker, indeed. That's one of the very important reasons to do the pilot. We're screening the intensive care unit two times a week and sometimes samples arrive on Thursday and when you finally get the results [using conventional culture techniques] it might be six days later. With Pathogenica, we are able to get the results in a shorter time.
And just how fast is that?  Rossen claims that they can go from having a bug isolated from the first patient to an RT-PCR based test in about a week.  That includes all the sample collection, handling, sequencing, bioinformatics, and decision making on the screening test.

That's pretty fast.

While he's optimistic that they can still shorten the time further to three or four days with improved sample preparation, faster sequencing, and better bioinformatics, developing better methods is still limited by having the right people and expertise available, a statement very similar to what I said a few months ago; the critical resource for this level of medicine is the people.

This work is definitely something to keep an eye on as their experience develops.

Thursday, September 19, 2013

Avoiding Traps and Finishing Research Projects Faster

Why do some scientific researchers excel in big team based projects while others prefer to work on solitary ones?  Is it a personality trait or something that's taught (or not) during graduate school and in early years of people's careers?

Bernard Tulsi put together a kind of humorous, but accurate, portrayal in Lab Manager magazine of how managing people doing research is like herding wild cats.  Most of the points stem from an interview with Vish Krishnan, a professor at the Rady School of Management, UCSD, who says:
“The challenge in a lab management setting is that we are dealing with a highly trained and knowledgeable workforce—so you can’t use hierarchical management techniques.”

He is careful to point out, “This is not exclusive to the lab but can be seen in other types of knowledge-oriented businesses as well. .. These workers are highly capable and extremely talented, and that is an important part of the challenge in getting their cooperation and commitment”
Strong traits like individuality, competitiveness, and turf protection are pointed out as negative elsewhere in the article and it's suggested that the general style of scientific training is partly to blame. 

I doubt negative traits are taught anywhere as part of formal grad school training, but rather it's the lack of training in skills required to work on teams that's the root cause.  Working in a team is very different than flying solo.

A research lab: That untameable workplace filled with huge egos.
There's also this zinger:
“Labs are very much the prototypical example of the highly trained, high-ego workforce type.”
Was this supposed to describe a workforce filled with recalcitrant narcissists?

I imagine this kind of lab as one filled with Alphas ready to bludgeon competitors (and each other) using pipettes and keyboards to secure control over a source of data.  Thankfully, this situation is ridiculous and one I've never been in and have rarely seen, even after working in multiple institutes and departments.

Are scientists highly trained?  Definitely.  Do they want to be successful with their work?  Absolutely.  Do they think they're more important than they actually are?  Usually not.  So I'm against the stereotype of labs being run by highly trained cowboys, with no offense meant to actual cattle farmers.

Again, Academia versus Industry

Lastly, there's this observation:
“From my experience, academia seems to have a worse problem than corporate entities.”
So yes, while a lab environment can definitely be dysfunctional, I don't think it's as much to do with whatever training people had in academic settings, but of motivations inherent in the job each person holds.

Everyone has personal goals for their work, and sometimes those goals conflict with the needs of the business or lab.  This is especially true in academic environments, where many people are trying to get degrees or publish papers - uniquely personal goals - so if the person managing research doesn't structure each person's project with the whole picture in mind, the group ends up being an entity that's not firing on all cylinders, as Krishnan observes.

If you want a research project to succeed, you need to try and motivate each person with research that fits their personal goals.  It doesn't matter what those goals are, but you need to make sure you're helping people hit them.  In doing so, and if you've structured the work correctly, hopefully they'll hit yours too.

All that said, there are several memes that circulate in academic environments which can lead to the teamwork problems described above if they're misinterpreted by people early on in their training. 

Here are two major ones that can lead people in training astray:

You need to establish yourself as independent.

Almost everyone should strive to establish themselves as independent.  Jack Welch, ex-CEO of General Electric, once quipped that you could give someone lifetime employability, but not employment, by training them and giving them skills to go elsewhere.  In academia, students are sometimes told to demonstrate that they're independent, and instead of trying to build and defend their own independent opinions, they extrapolate 'independence' to mean that they should not be dependent on help from anyone.

And so, some students gladly go off and spend hours and days learning to do everything technical themselves to demonstrate "independence".  The result is that they learn to do everything, but since they don't do anything particularly well (or efficiently for that matter) their progress slows down.  Of course, no one notices because they're not communicating with anyone.

At this point, I'd say the fault starts to fall back on their manager or PI, who should set them aside and say "Hey, I understand you want to learn everything, but let's pick one or two areas of expertise for you and you'll become the expert in that around here.  Joe and Jane over there are pretty good at all the other techniques so let's split up your work."  Trading a little work here and there doesn't really hurt anyone.

Division of labour isn't anything new, but it's not taking advantage of the concept that's a scientific management mistake.

Eventually, the real danger of "having to establish yourself as independent" is that people stop helping others who don't help them, so eventually no one is helping anyone and the research group naturally breaks down.

Someone is trying to scoop you.

Here's another concept that's more damaging than it is helpful.  Unless you're working on something that can be replicated exactly somewhere else: defining a protein-protein interaction; purifying an enzyme complex; determining a protein structure; explaining something about a particular mutation, no one is trying to scoop you.

Surely, someone else is trying to beat you to the goal, but that someone is very likely elsewhere and probably not in your lab or even your department.  Your co-workers are very unlikely to take your work and sell it to the competition, at least not in an academic environment.

So if you're showing around slides containing your work, you don't have to throw in a non-disclosure condition (I've actually had that happen to me), because frankly, most people are too busy with other projects to hatch plots and steal your data.

The only thing secrecy to protect against being scooped is guaranteed to yield is a delay in finalizing your work.  Students afraid of being scooped end up hoarding data, and in the end finish projects months or years later than if their colleagues helped them all along.

And ironically, they risk getting scooped.

Thursday, September 12, 2013

September 2013 Readership Drive

I want to take this chance to send many thanks out to all the people that read the site, post comments, and all that sent in suggestions or links to post.  It's a pleasure to get all the kind words of feedback from everyone that finds Checkmate Scientist an enjoyable site to visit.

Here's my chance to remind you that there are several easy ways to stay up to date with Checkmate Scientist:
As the web site grows over time I look forward to writing more full length articles for you to read, but need you to email, tweet, or otherwise share your favorite articles with your friends and colleagues.

Thank you so much for supporting Checkmate Scientist with your readership.

-- Paul Krzyzanowski

Adapting to Sequestration, It's Hard But Possible

Dave Levitan, at Scientific American, writes:
With NIH funding dropping and few alternate sources of income—remember that tuition, massive endowments and other bank account stuffers support the Harvard Medical Schools of the world—virtually all independent institutes are in some kind of budgetary trouble.
Levitan's article is excellent at making the point that some research scientists (and institutes) that mainly rely on government funding are in dire straits in this cycle of cutbacks.

This is a sore point, but it kind of surprises me.  In our early years, we're often told to not put all of our eggs into one basket, a saying which later morphed into the whole concept of diversification, applying to seemingly everything from making investment choices to building a portfolio of research projects.

But one point not really emphasized enough is that revenues need to be diversified too.  Whether you're concerned with business revenue (grants, contracts, clients, or product lines) or personal revenue (two or more incomes per household, consulting, independent wealth), there's risk related to the number of revenue streams, their source, and their quality.

Which suggests that the problems arising with the current climate of science funding and sequestration are one great big example of putting too much faith into a concentrated revenue stream that happens to be more volatile than previously assumed.  If there's anything this cycle of fiscal tightening is teaching a new generation of scientists, it's that government funding is actually pretty volatile and comes with many added risks associated with it; Like the potential to completely destroy your research program's momentum when it disappears. 

Taxes are guaranteed; tax funded science is not.

It doesn't actually have to be that way, as the article points out.  Adapting to Sequestration, or any period of tight money is hard, but possible. 

You can always merge institutes with universities (Homework question: Who gets the money from the sale of an institute?) but ultimately you're just switching one revenue stream (government) for another (wherever the university's funds come from). 

(And before someone writes to point out an error in logic, it's true that some university money comes from taxes, but it's already one step removed from other demands on the public purse.)

For researchers, switching one source of revenues for another probably isn't the most terrible thing to happen - you can still do good work - but when the switch comes it's the different responsibilities attached to the new money that cause the problems, not the fact the old money is gone.

Whether individual scientists have been taught to adapt to the new strings is another matter altogether.

Monday, September 9, 2013

On Keeping Pre-Meds Away From the Lab

Terry McGlynn, commenting at Small Pond Science:
What criteria do you have for bringing in premeds to do research in your lab? 
There are so many reasons to keep away from premeds. For starters, premeds are more prone to:

  1. Want research “experience” but don’t want to do actual research
  2. Drop lab duties at the drop of a hat whenever an A- might happen
  3. Walk away as soon as they think their stellar recommendation letter is a lock
Ouch. Then again, I too have seen these things happen.. more than once.

I'm not really sure why Reason #1 is the most common; I've always imagined that people on medical admissions committees see through "research experience" that's spun to the benefit of the applicants a kind of window dressing, especially when it might span only four months.

The main positive feature of having premed research experience might be to say "been there, tried that" in an admissions interview and that you're there because you didn't research that much.  It's not world-changing but at least it's honest.

Wednesday, September 4, 2013

The Most Classic Mistake in a Science Presentations

William Ronco, at GEN, shares ten mistakes seen in science presentations:
We can and should expect much more of science presentations. We depend on presentations to carry out the organization’s core business, develop ideas, test hypotheses, and explore alternatives. Yet we accept levels of presentation effectiveness we wouldn’t begin to tolerate in the other, technical aspects of our work.
Agreed, but many times "the organization's core business" is ill defined.  Nevertheless, #5, Data Overload is the worse offender, in my mind. 

How many times have you watched someone flash through every figure they've created since the last time you met?  How many times have you committed this crime?

Yes, you're allowed to withhold some of your data, as long as it's not essential to make your point and you're not concealing data that argues against your case.  You can always share it your next opportunity.

When it comes to communication, oftentimes less is more.