P in streams

I work with some incredible grad students at the University of Alaska Fairbanks. Today, I’d like to highlight research led by Sophie Weaver, a student in the Biology & Wildlife department.

When asked about her research, Sophie likes to say she studies “P in streams.” Sophie is investigating how differences in nutrient availability might affect the growth of the organisms that make up the green scum, or microbial skins, that one slips on when crossing a stream. Besides phosphorus (the “P” in her descriptive quip), she also works with nitrate, ammonium, and acetate.

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Sophie with her little blue cups.

After adding various nutrients to little blue cups, she launches them in her research streams. Post-incubation, she collects the cups to measure the abundance of autotrophs (critters that produce their own energy) and heterotrophs (critters that, like us, consume delicious things to produce energy). The ratio of autotrophs to heterotrophs can tell her something about how nutrients impact green scum composition. This research is important because stream microorganisms directly influence water quality and ecosystem function.

Sophie conducts her research at the Caribou-Poker Creeks Research Watershed (CPCRW), a pristine watershed located about thirty-five miles northeast of Fairbanks. Rumor has it that Sophie and her labmates been known to pursue the other wonders of CPCRW besides what fuels green scum growth, from chilling ciders in wee arctic streams to stripping down, jumping in, and cooling off on a “hot” Alaskan summer day.

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To freeze or not to freeze: insect overwintering strategies

Perhaps winter hasn’t quite yet crawled up your windowpanes or stretched its fingers across your favorite pond, but it’s certainly making its presence known at latitude 64°N. I’ve been pulling out extra quilts, wrapping up in scarves for my morning bike commute, and making more baked goods to keep up with my hot chocolate habit.

As a graduate student, I study the molecular story behind arctic ground squirrel hibernation at the University of Alaska Fairbanks. I’m the first to admit I’m a mammal kind of gal⎯I gravitate towards the furry and fuzzy and revel in soft fur, large eyes, and squeaky-cute chirps. However, every now and then I step outside of my mammalian bias and remember that there is a world of tiny, crawling, wiggling creatures that are surviving the cold in ways that are equally as extraordinary as the strategies employed by my favorite hibernating rodent.

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Arctic ground squirrel hibernating in the lab. So cute. Copyright © 2013 Øivind Tøien/Institute of Arctic Biology.

I don’t think I’m alone in my mammalian predisposition. It can be easy to overlook insects, especially the more inconspicuous and less flashy species. However, during the Alaskan spring and summer, it is impossible to ignore the state’s most infamous insect: the mosquito.

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Mosquito (Culex quinquefasciatus) larva. Image courtesy of the CDC.

Growing up in Alaska, I never thought about what happened to mosquitoes during the winter. Perhaps I was simply happy they were gone, or maybe my gravitation towards the furry was present from a tender age. In any case, it wasn’t until I was in my late twenties that I learned there are two general types of Alaskan mosquitoes. One variety⎯affectionately called “snow mosquitoes”⎯overwinter in adult form. When temperatures start to drop, they tuck away in tree bark or bury themselves in the leaf litter and begin the process of supercooling.

You may have heard of supercooling, the process by which a liquid can remain liquid below its usual freezing point. A supercooled liquid must remain completely free of any impurity, as even a speck of dust can serve as a nucleation point for ice crystals to form. After snow mosquitoes rid their blood of impurities, they are able to survive winter temperatures as low as -31°C.

The adults of the other variety of mosquito lay their eggs in the fall. After depositing the next generation of blood-sucking babes, the adults do not attempt to make it through the chilly winter ahead and die an unmourned death. Their progeny hatch in the spring and are considered much more voracious biters than their cousins. (Interested in mosquito matters? Refer to the seminal 1949 book The Natural History of Mosquitoes by Marston Bates.)

(Quick mammalian aside: Arctic ground squirrels are the only known mammal to supercool. Similar to mosquitoes, they are also thought to remove their blood of impurities that would otherwise encourage ice growth. Arctic ground squirrels can lower their body temperature to -2.9°C, an incredible feat for an endotherm.)

Supercooling is an example of a freeze-avoidant strategy, in which an animal shifts its physiology to avoid the buildup of ice crystals in its blood. Yellowjacket queens living in subarctic Alaska also supercool. To avoid touching snow or ice, which can disturb a supercooled insect and promote instantaneous freezing, the queens hang by their mandibles from a twig or leaf stem in the leaf litter. The hollow space occupied by their hanging body creates a buffer of air between them and any dangerous frozen water.

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Vespula vulgaris, or common wasp, or yellowjacket. Image courtesy of JL Boyer.

Another equally impressive strategy employed by overwintering insects is freeze tolerance. Instead of preventing the formation of internal ice, these insects embrace it. There are various means of becoming an insect icicle, and most involve promoting crystallization extracellularly. Encouraging ice to form outside of cells protects the delicate machinery within cells, which carries clear benefits to the animal. One exception to this rule is found in the alpine cockroach (Celatoblatta quinquemaculata), which can survive temperatures down to -9°C and allows for the formation of ice crystals within its gut cells. It isn’t entirely clear how they achieve this feat, but it could be via thermal hysteresis proteins (also known as antifreeze proteins). These proteins widen the gap between water’s melting point and freezing point by shaping ice into protein-sheathed, faceted ice crystals. Employing a thermal hysteresis strategy decreases the insect’s lower lethal temperature. Other freeze-tolerant insects include the Isabella tiger moth (Pyrrharctia isabella) and the flightless midge (Belgica antarctica).

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The mechanism of thermal hysteresis via antifreeze proteins. Figure courtesy of Davies 2014.

It’s incredible to think about anything staying warm during a Fairbanks winter, much less a tiny mosquito or a wee wasp queen. To maintain my own endothermic heat through Alaska’s longest season, I use a variety of items and strategies, including down jackets, mittens, extra socks, toe warmers, heating oil, gasoline, wood stoves, hot chocolate, soup, quilts, and dog snuggles. Not nearly as efficient as some of my insect friends, but they will have to do.

Dynamic telomeres and the aging process

My name is Sara Wilbur. I’m a third-year masters student in biology at the University of Alaska Fairbanks.

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Me and my dog Junie biking the White Mountains trail. Photo credit: Jason Clark.

I’ve written for NiB before, about work-life balance in academia, and yesterday I was introduced as the newest contributor to NiB. I’m very excited to write for this wonderful project! You can expect future articles to focus on telomeres, arctic ground squirrels/hibernation, and scientific life in Alaska.

Aging, DNA structure, and the dynamic telomere

The mind simplifies difficult concepts to support graspability. One example of this tendency is found in our attempts to define the aging process. Aging is complex, nuanced, and expressed differently across individuals. It would be quite useful if there was a quantifiable “thing” in the body that indicated how long an organism had left to live. In the mid-1970s, a discovery came that presented itself as a solution to the problem of measuring age: protective, terminal chromosome sequences known as telomeres.

2930423615_5320362dea_o.jpgAging is complex and nuanced. Photo credit: Flickr.

As is widely understood, DNA provides the molecular “blueprint” for all organisms, influencing what they look like and how they behave. The particular nucleic acid sequences (the Ts, As, Gs, and Cs) of an individual’s DNA codes for specific proteins, which are involved in virtually every cellular process. However, of all the DNA you have, only 1% of DNA contains coding sections. Initially considered “junk DNA,” the remaining 99% of noncoding DNA fulfills many important functions, including transcriptional regulation (turning genes “up” to make more of a particular protein or “down” to lessen protein production) and DNA protection, a duty fulfilled by the dynamic telomere.

Telomeric duties

Telomeres have two main purposes. One is to maintain chromosome integrity. If you’re a molecule of DNA, a double-strand break is cause for alarm. Fortunately, DNA repair enzymes are recruited to double-strand breaks, allowing DNA to replicate properly and be transcribed faithfully. However, if you think about it, a chromosome end could be seen as a double-strand break. What prevents chromosome ends from being unnecessarily repaired? It turns out that telomeres aren’t simply naked DNA sequences, but are instead intimately associated with several proteins in a complex known as shelterin. Shelterin proteins help the telomere fold back and associate with itself. This forms a “t-loop” to essentially hide and protect the chromosome end.

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Telomeric structure and associated proteins. Figure credit: Blackburn et al. 2015.

Perhaps more famously, telomeres also act as a buffer to prevent coding DNA erosion during cell replication. An important consequence of the evolution of linear chromosomes (found in all eukaryotes, from yeast to elephants) is that a few nucleotides are lost with each round of cell division. The DNA replication machinery cannot fully replace the outermost nucleotides, so the DNA strand gets shorter over time. As it is the telomere sequence that caps chromosomes, it is these sequences—rather than the DNA in between—that take the hit.

How are telomeres implicated in the aging process?

Telomere shortening over time is thought contribute to the aging process. Before I describe why this might be, let’s explore a more fundamental idea: what is aging, anyway? Basically, it’s a loss of physiological—or bodily—function. A proposed root case of declining functionality in the body is cellular senescence, or when a cell ceases dividing; a buildup of these cells within a tissue is associated with aging. Telomere shortening is one cause of cellular senescence: when telomeres reach a critically short length, cells cease to divide. This is a mechanism to prevent cells from becoming cancerous. However, there is a tradeoff: a buildup of senescent cells that can no longer induce tumor growth could be driving the aging process.

Telomere length does change with time, but shortening is also influenced by lifestyle and genetics. Some species have “mega-telomeres” (including mice, which are a common model for in vivo telomere length research), which have a different biology than more run-of-the-mill telomeres (as we humans possess). To further complicate matters, some species possess the enzyme telomerase in their body cells. This enzyme replaces lost nucleotides, essentially preserving telomere length over time. However, telomerase isn’t the answer to short telomere’s prayers: 80 to 90% of all cancers are associated with over-active telomerase activity.

The future of telomere research

The initial excitement surrounding telomeres’ discovery forty years ago and the potential for its use as a simple biomarker of aging and disease are still with us today. However, like any biological process, telomere dynamics are much more complicated than we first thought. For instance, while there is overwhelming evidence from the past few decades that telomeres do decline with age across species, it is still unclear if telomere length can accurately predict calendar age. The future of telomere research will continue to evolve away from cell culture work into living systems, and from common laboratory animals to a wider species diversity, including ectotherms (“cold-blooded” animals), plants, and hibernators. Stay tuned for more on telomere dynamics in these “non-traditional” organisms!

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What’s happening in hibernator telomeres? Juvenile arctic ground squirrel hiding in some willows. Photo credit: the author.

New school year, new contributor!

This blog started as a collaborative effort. As we all advanced in our careers and grown families some regular contributors have become irregular contributors, and I have been the primary curator for sometime.

UNTIL NOW!

Sara Wilbur reached out asking to write a guest post. We worked on getting her delightful post out together.

And with the new school year, she’s back for more! She’ll be writing about artic squirrels and telomeres and quirky scientists and life in Alaska.

And I’m thrilled that’ll she’ll be posting on Thursdays. Welcome to the NiB family!

 

In 1974, They Gave The Nobel To Her Supervisor. Now She’s Won A $3 Million Prize

One of the great misconceptions of science is that great discoveries start with a “Eureka!”.

More often than not, great discoveries start instead with a “that’s funny/odd/strange, I wonder what’s going on here”. And that’s what happened to Bell Burnell. She and her graduate supervisor, Antony Hewish, built a radio telescope to observe strange objects in distant galaxies known as quasars. It printed the data as a line (using red ink) across ~100 feet of paper per day. And in pouring over that data, Bell noticed something strange: “an unclassifiable squiggle”

The squiggle was soon identified as pulsars, rapidly spinning neutron stars that emit radiation. Finding them is considered one of the greatest astronomical discoveries of the 20th century. So much so that it won a Nobel Prize… for Bell’s advisor.

However, she gets the last laugh: 50 years after the “unclassified squiggle” in red ink, her discovery has earned her a Special Breakthrough Prize in Fundamental Physics, which comes with a check for $3 million. Dr. Burnell is donating her prize winnings to the U.K.’s Institute of Physics, where they will fund graduate scholarships for people from under-represented groups to study physics.

“I don’t want or need the money myself and it seemed to me that this was perhaps the best use I could put to it,” she told the BBC, adding that she wants to use the money to counter the “unconscious bias” that she says happens in physics research jobs.

The astrophysicist noted there has been an upside to the Nobel snub all those years ago.

“I feel I’ve done very well out of not getting a Nobel prize,” she told the Guardian. “If you get a Nobel prize you have this fantastic week and then nobody gives you anything else. If you don’t get a Nobel prize you get everything that moves. Almost every year there’s been some sort of party because I’ve got another award. That’s much more fun.”

Read the full story here.

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Getting into industry: LinkedIn Profile

This post continues my discussion about academia to industry, and how I got from there to here. 

I made the decision to get a job in industry, and was immediately faced with the task of: “Ok, now how do I do that?” And the sad truth is that it’s entirely different than academia.

You need people to be able to find you from the sea of other options/people. You then need them to want to talk to you. And only then, at the interview, is the job won or lost. So we’ll start where I started, putting yourself into the ether of the job market and making yourself known. And you start by developing a LinkedIn profile.

I know a few academics have a profile, but most people in academia haven’t taken it too seriously. But recruiters and employers really do look at them. And building a good one is non-trivial.

Pick a professional photo for your profile picture. Not a photo of you pulling frogs from your experimental ponds, or pipetting like a pro (unless you’re applying for a laboratory role). Get a headshot of you looking smart and professional. And if you don’t have one, have one taken. Pay for it. It’s your first foot forward, and it’s worth the investment.

Write a “personal statement”. This should all be visible without someone having to expand the “more” tab. Make it catchy and easy to understand. “I’m a scientist passionate about making data driven decisions”. Or “I have spent a career focused on increasing understanding of statistics”

Next, list all your jobs. Include your PhD and MS and postdoc positions.  No need to go back before then, no one cares that you worked at McDonald’s in high school. (However, if you had significant work experience before or during your academic journey, consider whether the position might be relevant to include, especially if it involved related analytic work or was a management position.)  Under each position, list what you did in that position. This is remarkably similar to the academic PhD. Now, go back and make all your descriptions of your jobs entirely free of jargon. Make the bullet points simple and easy to understand. Each one should be no more than 10 words. NO MORE THAN 10 WORDS. You’re not trying to sound smart here, you’re trying to get a recruiter who has no or limited knowledge of science to know that you’re worth talking to. Feel free to steal language DIRECTLY from job posts of jobs you might want. If the job post says: “need to be able to multitask in a fast paced environment” write “I am able to multitask in a fast paced environment”. Plagiarism is ok here. Get used to this, you’re going to do it in a few different steps in the getting a job process.

Finally, go look at jobs you’re interested in on LinkedIn. At the bottom of each job, it says “you have X skills in common with others that are looking at this job”. Click on that and find out what skills your profile is missing. Do you have those skills too? Add them to your profile. Remember when I said it’s ok to plagiarize to some extent. When you have desired skills, you need to make sure other people – especially the job recruiters – know you do.  (NOTE:  there’s a huge difference in plagiarizing the description of the skills you have and making up a skill set you don’t have.)

Next week,  we’ll go over how to make your resume good enough that recruiters looking at you will want to talk to you.

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My current LinkedIn headshot. Photo credit to my great good friend Cat Thrasher

Moving into industry: You have most of the skills you need, and you can learn the few you are missing

Continuing on in my series about academia to industry, here’s how I gained the skills I needed to get a job outside of academia. 

I mentioned last week that I decided I wanted to leave academia, but then spent a full year preparing and learning what that meant. I started with an understanding that I had lots of skills, but wasn’t sure how those would translate to anything besides academia, and I ended with a job in industry. So I’ll start with the skills you do have that are applicable (yes, even you) and then talk about which ones you should make sure you have before starting to apply:

Skills you have:

Teaching and Presenting: One of the most critical traits desired in people who work with data is that they are able to communicate that data to other people. It is RARE in industry to have someone who can do the analyses AND communicate those to shareholders and relevant decision makers without them scratching their heads in confusion. But the good news: your entire academic career has prepared you for just this event. You know all those times you taught, and tried to get undergraduates to understand what you were talking about? Or those conferences where you build presentations to best present your results? They are training for doing that very thing which is VERY valuable in the world outside of academia. You already have this skill (high fives all around!).

Statistical knowledge: I know I sound like a jerk when I say this, but since moving into industry, I keep having to revise the list of things that I though everyone knew. I was asked “what is a p-value?” my first week, and have been asked more basic questions about statistics than I was ever asked in academia, even teaching undergraduates. You have a career full of experience doing experiments, collecting data, and making sense of that data by running statistical analyses. You are already ahead of the game in this regard.

Working hard: My mother told me: “The beginning of every job is the same, nose to the grindstone and work hard” and she was right. You need to be able to work harder and longer hours than you’re used to. Think back to your PhD – hours like that, only longer and in one place. The good news is that most of the multi-tasking you used to need to do is off your plate for now. The bad news is that means you have 9 + hours a day to focus on one task. Luckily, you’ve been doing this for awhile, and being a workaholic comes naturally to you. And even more luckily, this is not a long term commitment. You need to work hard at the beginning, but I’ve found it rare for people in my company (and other companies my friends have moved into) to work outside of regular business hours. This is a sprint, not a marathon.

Teaching yourself: In industry, they don’t expect you to be ready to go out of the box, like they often do in academia. In most industry environments, there is this whole process called “onboarding,” which I had never heard of before I started my current job. They know it’ll take you a little while to get up to speed. But, as an academic you’ve got a lifetime of experience teaching yourself. Great, it’ll serve you well. You can spend those first few months learning the things you’re going to need to know on the job, and that ability puts you ahead of your peers.

As you can see above, we’re already qualified, and have the potential to be successful. But you do need a few skills, at least for data science, that it is unlikely you have already attained. However, you can pick them up. Here are a few:

Python: Most academics I know code in R. The fancy ones also code in another language (I wrote simulations in C++)(I am not fancy though). While R is an excellent language for data analytics, and arguably the best in the world for statistics (come at me), for getting code into production, you need to know python. It’s similar to R, so shouldn’t be too hard, and if you have experience using R as a object oriented programming language, then it’ll be easier still. I learned python using DataCamp and strongly recommend it as a great resource. It has a jupyter notebook embedded in the site so you’re able to run code as you learn (learning by doing is important for me). But there are also books, youtube videos, coursera and udacity, and probably flash cards that will also help you learn this skill.

Querying database: In academia, we work on what are called “flat” data files, like CSV or excel files. Once you are looking at customer data, a flat file is simply too small. It’s like trying to open a NGS sequencing file – it’s just laughably impossible to do with your computer. So you need to learn how to query a database with schemas, like Postgres or MySQL. It’s a pretty simple programming language, and you’re going to learn to love to join tables. But this is a skill of taking a mountain of data, and finding the flower you want to study amidst the rocks. It takes time and practice, but is learnable.

Docker: I know I’m focusing a lot on the technology, but it has stood out to me as the one thing that’s very different between industry and academia. Docker is a way to run code locally that is entirely repeatable elsewhere. Your code is run within a “container” that is created with all the things you need installed in it, in a requirements file and an image. If you’re missing a requirement that exists locally on your computer, but is not in your file, the requirement won’t run. As a result, with the code base alone, you’re able to entirely reproduce your work, regardless of where you’re running your code from. When putting your work into deployment, this is especially awesome because you know that your container is ready to go before you deploy it. And it’s becoming ubiquitous across industry, so jump on that container bandwagon and download docker.

I’m sure I’m missing some things, and there are wealth of cultural differences that I haven’t even approached (feel free to comment below). But the gist of this post is that you ALREADY HAVE MOST OF THE SKILLS YOU NEED TO SUCCEED IN INDUSTRY. And the ones you lack are trival to learn. Now that we know this,next week we’ll talk  about one of the things you need to do to actually apply: a linkedIn profile.

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You’ve presented your research, you’ve taught. You know how to communicate results, you’re already ahead of the game.