Woodpeckers can ring and run too

Our Ring Pro video doorbell recently caught this red-headed ding-dong ditcher visiting our front door:

We answered this ring, but we weren't around about an hour later when it returned!

After checking with our neighbors, we found that gila woodpeckers have apparently been terrorizing some other doorbells and surveillance cameras in the area as well. It is pretty adorable... until you have to replace the device.

In this case, hopefully being startled twice by the Ring's chime is enough to encourage it to move on!

Tribute to Mom, Eileen Pavlic (1942–2019)

My mother, Eileen Pavlic, died on Tuesday, August 6, 2019, due to complications from a stroke. She had been battling cancer for a year, and immunotherapy had been working well to reduce the extent of her cancer. She was enthusiastic about the progress of the treatment and had even started to buy new clothes, anticipating at least another year of life. Then, surprisingly and very sadly, she suffered a stroke. We learned that metastatic cancer is a risk factor for stroke, and so this outcome was not a complete surprise to the doctors even though it was devastating to us. She was buried in an outfit that she had recently ordered and had never had the opportunity to wear herself.

We know she was very impressed and encouraged with the immunotherapy she received, and so I hope you will consider donating to the Pelotonia Institute for Immuno-Oncology (PIIO) or another reputable charity advancing the use of immunotherapy for the treatment and possible cure for cancer. 

I shared the comments below during her funeral on August 9, 2019. You can also read her obituary on-line.

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A Son's Tribute to His Mother, Eileen M. Pavlic (September 22, 1942 – August 6, 2019)

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Looking back on your life with someone is less like watching a movie from beginning to end but more like drawing lottery balls from a tumbling urn. There’s one random memory. Then another. Then another. It is only after drawing enough of them that we can look back on them, sort them out, and start seeing hints of a bigger picture. That’s how it was for me when thinking about what to say today, and I only hope this story comes across a little smoother than those lottery balls.

She always wanted me to become a medical doctor, and I was excited to oblige when I was younger. Books and models mom and dad would get me about anatomy and physiology were interesting to me, but ultimately, I think the real source of the appeal was not the content so much as the connection with her. At some point, I pivoted away from medicine toward things like mathematics, computing, and, ultimately, engineering. Even mom might not see how this has anything to do with her, but from my perspective looking back, I remember the young me watching her navigate a DOS prompt, manipulate revealed codes in WordPerfect documents, and designing merge fields for office automation, and she became my model for someone who could learn to use tools around her to do great things. She never viewed herself as a teacher – she would often excuse herself from philosophical discussions that dad and I would have by saying that “she didn’t go to college” – but she was one of my greatest teachers. Today, I teach college students, and I only hope that some of them could be as motivated by me as I was by her. And it’s no surprise to me that biology has shown up in a lot of my engineering research, which sometimes makes me think that the younger version of me is still trying to turn my PhD into an MD to make mom proud.

So, yeah, looking back on life with mom is a little like drawing lottery balls. And by giving me the opportunity to do so, mom’s given me one last lesson – that being her son was winning the jackpot.

Long, non-coding RNA serve an important role in the regulatory pathway controlling human skin wound healing

It seems like there has been a lot of cool wound-healing research results published lately. At the end of March, there was the result that bacteriophages hide their bacterial hosts from being cleared by mammalian immune response. Last week there was the result that aphids soldier nymphs use their own wound-healing proteins to plug gall holes. And now there's this new result on the mechanism underlying the role of long, noncoding RNA (lncRNA) in wound healing of human skin.

So what's long, non-coding RNA? The DNA in the human genome has to be converted to RNA, in much the same way computer code might be converted into assembly language, before it can then be converted into proteins (like assembly language being converted into object code that can actually be executed by the computer). Up until recently, biology has focused entirely on this pipeline -- DNA to RNA to protein. However, there are some bits of DNA that stop at RNA, which are termed "non-coding RNA" because they don't end up producing any proteins. They just float around until they naturally break down. However, there are hints that they are not merely intermediate transitional objects and that they can be functional molecules themselves. One of those hints is that they can be consistently turned on ("up-regulated") in certain situations. However, this doesn't mean that they're actually doing anything. There are technical details that make it useful to distinguish whether these non-coding RNA are short or long. I won't get into those. Just know, for the reset of this post, that "lncRNA" represents "long, non-coding RNA", which is a type of these mysterious molecules. When healthy human skin is wounded, the normal response happens to involve the up-regulation of one of these lncRNA's is consistently generated more than in undamaged tissue, but no one knows why.

In this paper, they show that the molecule actually sequesters other mechanisms within the tissue that would normally turn off the migration of cells important for wound healing. So the lncRNA's are functional -- they get turned on when the skin is wounded, and that sets of a chain reaction that disables other mechanisms that would normally keep wound healing processes turned off. So the lncRNA's are an important step in the regulatory network. That's very cool. It not only helps scientists understand wound healing but also one more way in which lncRNA's can be functional -- not just junk molecules floating around waiting to be broken back down.

Here is the primary source:

"Human skin long noncoding RNA WAKMAR1 regulates wound healing by enhancing keratinocyte migration"
by Li et al.
PNAS (2019), early edition
https://doi.org/10.1073/pnas.1814097116

Significance
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Although constituting the majority of the transcriptional output of the human genome, the functional importance of long noncoding RNAs (lncRNAs) has only recently been recognized. The role of lncRNAs in wound healing is virtually unknown. Our study focused on a skin-specific lncRNA, termed “wound and keratinocyte migration-associated lncRNA 1” (WAKMAR1), which is down-regulated in wound-edge keratinocytes of human chronic nonhealing wounds compared with normal wounds under reepithelialization. We identified WAKMAR1 as being critical for keratinocyte migration and its deficiency as impairing wound reepithelialization. Mechanistically, WAKMAR1 interacts with DNA methyltransferases and interferes with the promoter methylation of the E2F1 gene, which is a key transcription factor controlling a network of migratory genes. This line of evidence demonstrates that lncRNAs play an essential role in human skin wound healing.
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Abstract
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An increasing number of studies reveal the importance of long noncoding RNAs (lncRNAs) in gene expression control underlying many physiological and pathological processes. However, their role in skin wound healing remains poorly understood. Our study focused on a skin-specific lncRNA, LOC105372576, whose expression was increased during physiological wound healing. In human nonhealing wounds, however, its level was significantly lower compared with normal wounds under reepithelialization. We characterized LOC105372576 as a nuclear-localized, RNAPII-transcribed, and polyadenylated lncRNA. In keratinocytes, its expression was induced by TGF-β signaling. Knockdown of LOC105372576 and activation of its endogenous transcription, respectively, reduced and increased the motility of keratinocytes and reepithelialization of human ex vivo skin wounds. Therefore, LOC105372576 was termed “wound and keratinocyte migration-associated lncRNA 1” (WAKMAR1). Further study revealed that WAKMAR1 regulated a network of protein-coding genes important for cell migration, most of which were under the control of transcription factor E2F1. Mechanistically, WAKMAR1 enhanced E2F1 expression by interfering with E2F1 promoter methylation through the sequestration of DNA methyltransferases. Collectively, we have identified a lncRNA important for keratinocyte migration, whose deficiency may be involved in the pathogenesis of chronic wounds.
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Stochastic versus random: The difference is whether you're describing a model or a focal system

It seems like there is a lot of confusion in quantitative modeling circles over the relationship between "randomness" and "stochasticity." This is in large part because the success of stochastic models to make sense of the world around us has led to wide use of stochastic modeling, so much that "stochastic" has become a near synonym for "random." However, the two terms are not identical.

Slide from SOS 212 (Systems, Dynamics, and Sustainability), an introductory modeling course I teach

"Randomness" is a general term representing a special kind of uncertainty where the frequency of possible outcomes can be described using probabilistic methods. Where a photon is going to be at a particular instant of time is fundamentally random; it can only be described using a probability distribution. There are some kinds of uncertainty that cannot be described probabilistically. For example, I might know that an experiment can end in one of three different ways, but exactly how it will end depends upon which participant is performing the experiment. If I need to describe how the experiment can end but do not have any ability to specify a probabilistic weighting of the outcomes, I can describe the system as having a non-deterministic outcome. So something can be fundamentally random or non-deterministic. If it's random, you have some way to describe how one outcome might be more likely than another.

"Stochastic" is a term originating from Greek for "guess" or "conjecture." It describes a modeling technique to simplify a model by assuming (guessing/conjecturing) that the system being modeled is random, even if it is not. In other words, rather than having to account for a wide range of degrees of freedom that might be necessary to specify how a system is going to evolve, you substitute all of that detailed deterministic modeling with a crude approximation of the outcome being "random". The modeling burden then shifts from getting everything right down to high levels of precision to shaping the probability distributions to best match the outcomes that are most likely, regardless of what the underlying mechanism is. So a "stochastic model" is one that describes a system using randomness regardless of whether there is any reason to believe that the randomness is fundamental. It is a modeling trick to add analytically tractability to models that would otherwise be prohibitively complex to be useful.

So you shouldn't refer to phenomena themselves as being "stochastic." It is OK to say that the phenomena is random (if you really believe that). Or it is OK to say you use a stochastic model to describe the system (which implies that you will use randomness in order to omit a large number of degrees of freedom, thus making the model "simpler"). But you shouldn't use "stochastic" as a synonym for "random."

How to write a scientific paper, in four simple steps

Accomplished scientific writers will all have their own tips on how to go about writing a scientific paper. However, when you are first getting started, sometimes it is easier to have a set of steps available. Certainly there are many options available, but I personally have been finding the following set of steps useful when advising students lately.

• Step 1: Write the Introduction (starting with second paragraph)
  The introduction lays out why you bothered to do the study you did. It provides a background from the existing literature, and it points out holes in that literature or opportunities to test answers to questions that have little coverage.
  
  However, the first paragraph of your introduction (in fact, the first few sentences of your paper) are often the hardest ones to write. A colleague points out to her students that a great way to get around this potential writer's block is to simply skip the first paragraph. If you start with the second paragraph, you can immediately start laying out the argument for why your study needs to be done. Once you finish the rest of the introduction, it may be easy to see what the first paragraph needs to be. In other words, once you have 75% of the argument completed, the shape of the puzzle piece that remains will often be clear.
  
• Step 2: Write the Discussion
  If someone is already convinced that your paper has something interesting to say, that person will likely jump straight to the discussion and start reading there. This is where you describe how your data support some story that you want to tell about an inference you have made about the natural world. As you write your discussion, it will become clear exactly which data you need to tell your story. And so that brings us to the next step.
  
• Step 3: Write the Results
  Now that you have crafted a convincing discussion, you know what results are needed to support your argument. Writing the discussion first tells you what to include and what you can exclude. A lot of students make the mistake of starting with all of the possible plots that can be generated and then trying to string together a discussion around them, even if some of those plots really aren't needed. Don't do it that way. Instead, let your discussion guide which results to include.
  
• Step 4: Write the Methods
  Putting this step here is controversial. In many cases where your study has a clear experimental design that tests for something very specific, then it is possible to write your methods first. However, if you are instead working with a very large dataset that might someday result in multiple papers, then your methods might need to wait for you to determine exactly which results you need to make your argument. So once you've written your results, you can then generate methods that explain how all of those results came about.

Those four steps can often soothe writer's block on a scientific paper. You may determine a different flow as you mature in your writing process, but this is just one suggestion of where you might think about starting.

Special Note: Title and Abstract

Of course, two things that are left out of the steps above are the title and abstract. Young writer's often leave these until the end, and they often view them as complementary to the rest of the article. However, with maturity, it should be possible to write them first and view them almost as alternatives (or marketing pieces) for the whole article. Writing them first can also provide structure (similar to an outline) for the rest of the article. So work toward being able to write these two things first, but it's OK if you start by writing them last.

Keep in mind that most readers will only read the title and abstract (and some will only read the title). Consequently, the title should not be clickbait; the title should be an executive summary of the article, giving the punchline as opposed to begging the reader to read further. Interested readers will move on to the abstract. Consequently, your abstract should really have everything an educated reader needs to reconstruct your article; it should have a little bit of all of the sections. Your abstract should concisely say why you did the things you did, how you did them, what the main results were, and why those results are interesting.

Take-away Message: It's About the Reader

People are busy, and they allocate their reading resources accordingly. If you tease them with half statements, they will only get frustrated and move on to the next thing that competes for their time. Instead, give them something interesting and complete in the title. If they want more details, they can read the abstract, which should itself be complete. If they want more details, they'll read the main body (possibly starting with the discussion section). For the reader, it's about sequentially choosing what to read next. When you write, you should always have this in mind as you design the delivery vehicle for the content you hope will be disseminated broadly.

Stochasticity, Randomness, and Chaos (and the differences between them)

In popular culture, words like "stochastic", "random", and "chaotic" are often used interchangeably. However, these three terms have totally different meanings. Furthermore, "randomness" and "chaos" are near opposites. Whereas "randomness" is used to simplify the process of model building, "chaos" is a phenomena that comes out of non-random models. Chaotic patterns appear random but are products of entirely deterministic processes. Chaos is an extreme sensitivity to initial conditions that relates to trajectories from deterministic systems gaining more and more individuality over time, as opposed to less. If it is chaotic, it is not random.

I explain the similarities and differences between stochasticity, randomness, and chaos in this two-part lecture recorded as a complement to material in my SOS 212 (Systems, Dynamics, and Sustainability) course at Arizona State University.

Lecture G1: Randomness and Chaos (SOS 212, Systems Dynamics and Sustainability, ASU)

• Part 1: Randomness

• Part 2: Chaos

For related videos that may be of interest, see my SOS 212 YouTube playlist.

Bifurcation Diagrams, Hysteresis, and Tipping Points: Explanations Without Math

I teach a system dynamics modeling course (SOS 212: Systems, Dynamics, and Sustainability) at Arizona State University. It is a required course for our Sustainability BS students, which they ideally take in their second year after taking SOS 211, which is essentially Calculus I. The two courses together give them quantitative modeling fundamentals that they hopefully can make use of in other courses downstream and their careers in the future.

I end up having to cover a lot of content in SOS 212 that I myself learned through the lens of mathematics, but these students are learning it much earlier than I did and without many of the mathematical fundamentals. So I have to come up with explanations that do not rely on the mathematics. Here is an example from a recent lecture on bifurcation diagrams, hysteresis, and tipping points. It builds upon a fisheries example (from Morecroft's 2015 textbook) that uses a "Net regeneration" lookup table in lieu of a formal mathematical expression.

You can find additional videos related to this course at my SOS 212: System, Dynamics, and Sustainability playlist.

Early Career Academics: Tips for Preparing for your Interview Visit

This post goes out to early career academics about to interview for positions. Preparing for your interview is very different than preparing for just another seminar visit. Moreover, the observable difference between a well-prepared candidate and one who sees the interview as  just another invited seminar visit is HUGE. Do yourself a favor and ask faculty you know how to prepare. Most faculty, even junior faculty, have served on at least one search committee and will have a lot of good perspectives from sitting on the other side of the interview process.

This post gives a few tips that I've picked up after sitting on search committees. Your mileage may vary, and you should definitely ask around. But this is what I notice...

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Given that your audience will be with everyone you meet that day as well as everyone who sees your presentation (which may be video taped and viewed in a tiny screen, which is something you might want to consider when crafting it), you should survey several people to see what sticks out for them about good and bad candidates. Ultimately, people make up their minds very quickly based on relatively small things. The search committee might have combed through your CV, and certainly some who submit feedback to the search committee about your visit will have as well, but many will make their decision during your talk (or multiple talks, in the case of some departments) and/or in the few minutes they have had to talk to you during a half-hour meeting or over a meal. So get a good idea of what "many people" might like.

So below I've put some good general tips from my perspective. Again, your mileage may vary.

FIRST, THE NON-TALK-RELATED TIPS (talk tips in next section)

It's not all about the job talk. I've given job talk tips below, and the job talk is extremely important -- one of the most important talks you'll give. However, there is a lot of the day(s) that won't be the job talk, and you need to be prepared for all of the interactions you'll have. So I put the non-job talk tips first.

• DO YOUR (VIRTUAL) RECONNAISSANCE IN ADVANCE! When you find out you are going to be interviewing, you may not know who exactly you will meet that day. Even when you're given the schedule, there may be last-minute additions. So if you can, do your best to memorize who all of the faculty are and know roughly what they do (up to a 2-sentence description). You may not know who the search committee is, but you should use whatever clues have been made available in the communication thus far to be able to spot them in a crowd so that you know they're coming. You never want to be in the position where you have to ask someone what they do. You want to make it seem like you are genuinely interested in their research and can ask intelligent questions about it. You also want to try to anticipate what they find valuable and adjust your answers to questions to be complementary to these perspectives.
• Try to understand how the school is structured and where your position would fit in that structure. Try to understand what resources are available. Be able to tell someone what specific things attract you to that school or that program. Make it look like you've been waiting for this particular position to open and jumped on the opportunity as opposed to just interviewing at any random school.
• Be very courteous and approachable and never dismissive. Be comfortable. Do your best to be relaxed and conversational while also internally keeping up a little bit of a guard. You don't want to be too quiet, but you want to be more professional than you usually are on an average visit. You aren't required to answer certain classes of personal questions (whether you have a spouse, kids, plans for these things, etc.), and so you can divert if they happen to come up casually, or you can try to use them if you think they would be an advantage. But don't give up too much information about things that could make it difficult for you to accept a position. Those tricky details can come out during negotiation.
• Keep in mind that you want faculty to want to work with you. You want them to be excited about collaborating with you.
• Keep in mind that you will also be evaluated based on whether there is too much overlap with existing faculty. Do your best to emphasize the new things you can bring (without accidentally pointing out weaknesses in other faculty or the program as a whole).
• You may be given 30 minutes alone with graduate students. Prepare for this time by having a set of questions that you can ask them (in case they don't have much to ask you). You have already studied up on all of their advisers, and so you should be prepared for the different research directions they have and can maybe anticipate some of their answers. But let them speak. You want them to see you as another faculty member that they would want around as a resource. Their advisers may ask them how they felt about the interaction.

JOB TALK TIPS:

And here are the general comments about job talks. See the previous section about non-job-talk related tips (in short: DO YOUR RECONNAISSANCE and BE COURTEOUS)...

• Practice your job talk before hand so that your timing is flawless and you are clearly confident with the material. And get your timing and talk density right so that you END WITH TIME FOR QUESTIONS. Ten minutes is sufficient. But don't leave more than 15 minutes. But if you leave less than ten minutes, people will not be impressed (and may even be annoyed).
• Make sure your job talk tells a consistent narrative that gives everyone in the audience an idea of what your research vision is and how your career up to this point has successfully implemented that vision. This may not be true a priori, but you need to find a way to tell your story to make it seem true. People don't want to see a random collection of research. They want to use the talk to get to know you, what you've done, what you will likely do in the next 3–5 years, and be impressed with both the current body of work and the potential. With that in mind, you don't have to present everything if some things don't contribute to the broad overall narrative. If you still have some work you're proud of that you don't think fits a narrative, you can include it briefly as a kind of "Other things that I am interested in" near the end to show you have breadth. But don't hop back and forth between disconnected projects. People will forget what you're all about and get frustrated by the lack of consistency.
• If your work has been published or presented at major conferences, call out these venues in your presentation as you go through them. You want people to be convinced that other people care about your work. Don't hit them over the head with giant bibs, but maybe include a small parenthetical ref at the bottom of slides here and there and then say things like, "In work I presented at .... last year," or, "In work that came out in ... a few months ago..."
• If you have more work than just your PhD work, be sure to show it somewhere. People like to get a sample of what you'll do as an independent researcher. Sometimes just the PhD work doesn't quite capture that.
• Include at least a few slides on future directions. You don't want people wondering what you'll do next. Pointing out where you have already received funding is a good thing, and it's definitely good to identify where you'll go next for funding (agencies or even particular programs). Some sort of flowchart showing how your research vision can be divided into specific threads that meet the objectives of these different agencies is great as it is more convincing that your vision can be operationalized for the next 3–5 years.
• Figure out if you are in a discipline (or even a school) that cares so much about education that a significant portion of your job talk should be associated with your classroom innovations and perspectives.
• When you get to the end of your talk and are taking questions, try to maximize for the quantity of questions. Don't dwell on one particular answer, and don't give one question too much time. Do your best to respectfully acknowledge the value of the question you were given, but try to table long discussions for "off line" discussion. This is the only time some faculty will have to interact with you, and you don't want to frustrate some who have questions by being too thorough with someone else's question. It also looks much better if you answer 3 questions than spending a long time on 1. So find a way to pivot quickly to another question if your first question is starting to take too long and prevent you from getting to others.
• The Dean/Head of School/Director may be in the audience. They may have a question. Take that question first as they may have to leave earlier than everyone else, and they also have an outsized role in hiring decisions.

Make sure you go to a bunch of job talks before your own job talk to see the diversity and try to guess which ones are good and bad job talks. Usually (but not always) more senior people give better job talks because they already have a good idea of what is good and bad because they've done it more and been a part of the decision-making process themselves. Contrast these more senior researchers with junior researchers who are definitely giving their first job talk. You'll notice consistent differences in the structure of the talk. I would say a good structure is something like...

• Here's my vision
• Here's a few projects that fit well together that show that vision
• Here is where that vision takes me in the future and who will pay for it
• Oh, by the way, here are other things I do too, but I don't have time to go into in detail
• Here are my perspectives on education (depending on the school/discipline, you may not have this section at all or it may be half of your talk)
• Here are 10–15 minutes I've made available for questions