I spent a large chunk of my graduate training researching and modeling novel pathogen spread, and I am currently teaching both a “Current topics in biological research” class and an Introduction of Evolutionary Biology class (wherein we discuss viral genomes and phylogenetic relationships)… so I have been thinking and teaching about COVID19 all semester (which just about coincides with the full timeline of COVID19 discovery and spread). Thus far most of my professing has been largely ephemeral—a slide here and there to relate some science to current real-world research. In this post, I hope to create a more permanent and updatable container of what we know and how we know it. I am not a virologist or an epidemiologist, so I will just write about general science here, and refer readers to “the experts” at the end of the post.

How can scientists infer the trajectory of viral spread?

Viruses are relatively simple. Typically just a few genes wrapped up in an envelope of proteins and fats (aside: this is why hand washing with soap is effective against COVID19. Soap breaks up fats—like the COVID19 envelope—and thus effectively destroys the virus). Scientists can harness the relative simplicity of a specific virus, that virus’ genome, and the virus’ mechanism and means of transmission, to make strong predictions of where that virus has been, and where that virus may go.

Where it has been.

How can we tell where a virus came from? I mentioned that each virus has its own “genome”—that is, a set of genes that control the various viral mechanisms. These genes get replicated every time the virus itself replicates. Now, if you have read my blog before, you know that I like to stress that gene replication is an imperfect process. The imperfect nature of DNA replication means that viral genes can accumulate differences from one another as the virus spreads among people. We can use these genomic differences—physical differences in the exact nucleic acids that constitute the viral genome—to build a tree of relatedness among viruses that had their genome sequenced.

The tree of relatedness (called a phylogenetic tree in evolutionary biology) demonstrates the historical interconnectedness of the viruses because mutations happen randomly, and accumulate one-after-the-other, meaning that if virus A and virus B share the same difference at the same location when compared to all other viruses, then A and B are likely descended from a relatively recent common ancestor. And if a set of viruses from location Y are newly sequenced, and found to all share the same genomic differences with a set of viruses sequenced last week from location X, then you can bet that the Y viruses are related to (and, maybe even potentially originated from) the X viruses.

You can explore a continually-updated phylogenetic tree of COVID19 over at https://nextstrain.org/ncov .

Screenshot of COVID19 relatedness from nextstrain.org. Top left: the phylogenetic tree, zoomed in on the strains sequenced in the USA. Each time the fork branches we infer the existence of some common ancestor (meaning that the actual sequences share common genomic variants that arose in this ancestor). The fact that all the WA sequences are “nested” within the same group demonstrates that they are related, and actively replicating (accumulating more variants) within the community (as opposed to all being introduced to WA independently, which would result in much less relatedness). Top right: These viral genomes in geographic space. Bottom: the approximately 30,000 nucleotides that constitute the COVID19 genome, and where the genomic variants that informed this phylogenetic analysis lie.

So, we can reconstruct the history of spread of a virus by pinning down the relatedness of viral genomes in space and through time. But, how can we predict where the virus will go?

Where it is going.

Again, we can rely on the relatively simple nature of the virus itself to make predictions about viral dynamics. By watching how readily and quickly the pathogen originally spreads throughout a community epidemiologists can calculate an average “basic reproduction number” that tells us, essentially, how many new infections may arise from a single current infection (without any sort of intervention). If this number is less than one, it means that on average each new case gives rise to less than one new case, and infectious agents die out. If the number is greater than one, each new case gives rise to more than one new case, and infections may spread.

One of the main goals of epidemiologists and public health officials during an epidemic is to get the reproduction number of a pathogen (sometimes now called the effective reproductive number because it is in the presence of intervention) down below 1 using various means of action. A major successful intervention for many viruses is vaccination, whereby you remove individuals from the “susceptible” pool and add them to the “resistant” pool, and effectively stop the spread of a pathogen in its tracks (one new infection cannot give rise to other infections if everyone who contacts the infected person is already immune!)

But, vaccination only works if the virus has been around for a while and scientists have had time to develop and test the efficacy of vaccines. What can we do about a novel virus, with no current vaccine?

One thing we can do, when we detect someone who tests positive for the virus, is track contacts of infected individuals and then isolate individuals that may be infectious. For instance, using a wide range of parameters initially estimated for COVID19, outbreaks in new areas were simulated to be stopped if a large percent of contacts of infectious individuals are both traced and isolated.

What if there is very little testing for the virus, and thus no way to trace newly infected individuals and their contacts? How can we slow the spread of a virus if we do not know exactly who has the virus? Other mechanisms, such as “social distancing”, also reduce the reproductive number of pathogens by changing human behavior to minimize the probability of both contracting and spreading a pathogen.

Why are scientists and health care experts advocating “social distancing”?

Imagine two scenarios:

1. A major city holds a large parade, millions of people show up from around the region, all standing shoulder-to-shoulder for hours.
2. A major city detects the presence of a novel virus at very small numbers within its population. Public health officials recommend social distancing, and thus parades and other large gatherings are cancelled.

It is easy to imagine how scenario (1) above would much more readily facilitate the transmission of a virus compared to scenario (2).

Once the spread of a new virus has begun in a densely populated area, slowing the spread of the virus is of paramount public health importance. Hospitals have limited capacity and medical resources are finite, so slowing the incidence of newly infected individuals maximizes the chance that hospital beds are available at any point in time, whereas rapid transmission of a virus can put all the strain of an epidemic on the healthcare system at the same time. This concept of “lowering the epidemic peak” is illustrated nicely in the figure below.

CC-BY-2.0 Esther Kim and Carl T. Bergstrom

The example of the parade I used above came from real life. During the 1918 flu season Philly hosted a parade that resulted in the rapid spread of the flu. Other cities practiced social distancing once they learned of the infection, and had better public health outcomes. You can see how St. Louis “flattened the curve” here: https://www.pnas.org/content/104/18/7582#F1

Data cleaning, analyses, and plots

If you are interested in the COVID19 case data, the team at Johns Hopkins University Center for Systems Science and Engineering has been curating and posting data here: https://github.com/CSSEGISandData/COVID-19. I have a public github repository here https://github.com/vcannataro/COVID19_data_explore where I clean and do some simple analyses with these data, and plot a set of figures to track confirmed cases. Some outputs are also collated in an html document.

Links to the experts

The points below are by no means exhaustive, and I encourage readers to suggest more resources! These are resources I have found useful while I stumble around the web and encounter COVID science.

• Dr. Richard Lenski has a great blog over at https://telliamedrevisited.wordpress.com . Dr. Lenski has been cataloging what we know and how we know it throughout the spread of COVID19, and I highly recommend you head over there for lots more information.
• Dr. Trevor Bedford (twitter: https://twitter.com/trvrb) has been tweeting and blogging about their + their colleagues’ analyses of the COVID19 genome and associated phylogenetics (updated live at https://nextstrain.org/ncov). Their amazing work was the first example (I personally saw) that demonstrated there may be cryptic transmission of the virus within the USA.
• Dr. C.T. Bergstrom has an informative twitter feed (https://twitter.com/CT_Bergstrom), actively counters misinformation, and has created open-source material about COVID19: http://ctbergstrom.com/covid19.html
• The CDC has information on preventing virus transmission at home and within communities https://www.cdc.gov/coronavirus/2019-ncov/community/index.html
• The Washington Post has a great simulation of an “SIR model” that illuminates how and why social distancing is effective in  slowing viral spread. Find the simulation here: https://www.washingtonpost.com/graphics/2020/world/corona-simulator/
• Johns Hopkins University coronavirus resource center (https://coronavirus.jhu.edu/) has resources for both understanding COVID19 and also tracking the spread of the virus.
• This interactive R shiny app lets you experiment with disease parameters and interventions.
• This video from 3Blue1Brown covers quarantine, social distancing, travel restrictions and more using an elegant compartmentalized toy model.

Earlier in the summer I wrote a post on “Books for new faculty” wherein I detailed all the books that current profs recommended to me as I start my new faculty journey (including an editable google doc, so go add some books if you have some in mind!). Now that the semester is a few days away I thought it would be appropriate to throw in my two cents. Over the last year I have read two outstanding books that made me say “wow, I wish I read that earlier!” One book, on the importance of sleep, would have been super appropriate to read as a freshman in college. The other book, on maintaining bouts of deep concentration in this world of constant distraction, would have been very useful on the journey from late college through my postdoc.

Why we sleep

https://www.goodreads.com/book/show/34466963-why-we-sleep

Back in college we would brag about how little we slept. The all-nighters we pulled in the Genesee Hall common rooms before the orgo exams. The late-night caffeine-fueled Milne library study sessions. The respect we would bestow upon one another after hearing about marathon weeks (“they only sleep 3 hours a night!”).

If only I had read Dr. Walker’s Why We Sleep beforehand—

“Sleep enriches a diversity of functions, including our ability to learn, memorize, and make logical decisions and choices. Benevolently servicing our psychological health, sleep recalibrates our emotional brain circuits, allowing us to navigate next-day social and psychological challenges with cool-headed composure.” –Dr. Walker in Why We Sleep

Enriches our ability to learn and memorize and recalibrate our emotional brain circuits allowing us to navigate social challenges? Sounds like sleep is exactly what the doctor ordered for a new college student!

Besides overviewing (with plenty of experiments and evidence to back it up—Dr. Walker is Director of UC Berkeley’s Sleep and Neuroimaging Lab) everything that sleep enriches, the author also details plenty of aspects of our life that a lack of sleep alters.

Sleep loss inflicts such devastating effects on the brain, linking it to numerous neurological and psychiatric conditions (e.g., Alzheimer’s disease, anxiety, depression, bipolar disorder, suicide, stroke, and chronic pain), and on every physiological system of the body, further contributing to countless disorders and disease (e.g., cancer, diabetes, heart attacks, infertility, weight gain, obesity, and immune deficiency). –  Dr. Walker in Why We Sleep

One message from the book that has stuck with me is how we develop a sleep deficit, a debt of sleep we owe our bodies, with any prolonged period of reduced sleep. A few nights of 5–6 sleep hours a night instead of 7–8 hours builds up this deficit, and then we live our waking hours with reduced functionality (decreased cognitive abilities, reaction timing, immune system function, etc. etc. etc.). And, perhaps most worrisome, is that driving with a sleep deficit is just as bad as driving drunk, and it is much more prevalent.

In summary: get some sleep!  Now, you may find yourself asking “how could I possible sleep 8 hours a night when I am taking 16 credit hours and working a part time job and figuring out what to do with my life?”

Well, perhaps Deep Work has some answers.

Deep Work

https://www.goodreads.com/book/show/25744928-deep-work

I think my social media usage is pretty cyclic. It typically follows the following pattern:

1. Wow, I’m spending a lot of time on my phone mindlessly scrolling through Twitter. I should cut down. I’ll log off and only sign in for important reasons.
2. Wow, I’m missing out on conference updates, new manuscripts, and amazing opportunities to share my research. I should check into Twitter more often.
3. See (1).

In Deep Work Dr. Cal Newport chronicles our ever-increasing connectivity to one another  (or, more appropriately, to our phones and the attention-grabbing algorithms therein) and how this is affecting our ability to concentrate intensely for long periods of time and do meaningful deep work. “Deep work”, he argues, is the bread-and-butter of our information economy. The author then prescribes several rules and ideas to help us recapture our attention span. Here are some that stuck with me:

1. Structure your day. Make an hour-by-hour schedule for yourself, and defend your time blocks reserved for your meaningful work. Be aware that breaks for “shallow work” eat into your attention reservoirs and make getting back into deep work all the more difficult.
2. Have a time of the day where you stop working, and I mean really stop working. After you finish your work for the day, have a shutdown sequence routine that signifies the end of the work day. Do not check email at home. Let your mind reset and it will be more efficient and ready to work deeply tomorrow.
3. Quit social media. Dr. Newport suggests we view social media as a tool, and as any good farmer knows, you need to evaluate the necessity and economic benefit of any tool (not just blindly adopt the usage of a tool because everyone else is doing it!). Contrary to current popular opinion, just because something increases connectivity and has potential to be useful does not mean it will create more benefits than damage for everyone. (Note to students: He does recognize that social media may be very useful for new students that are looking to meet new friends).

I recommend this book to anyone who is finding themselves a little too connected with attention-grabbing algorithm-driven content streams and a not connected enough with work they find meaningful.

A common message

How do we find the time to do meaningful intellectual work in a world saturated in algorithms designed to grab and hold our attention? Well, if my recent reads have anything to say about it, the first thing we need to do is get enough sleep so that we have a healthy bedrock for our concentration to take hold. Next, we need to reserve blocks of time to a single meaningful task—and within this block do everything possible to keep our concentration on that single task. And by “we” I mean “me” because it is time to practice what I preach and get to work (the semester starts tomorrow!) Good luck everyone, remember to sleep!

I have spent a sizable chunk of my life in a profession that requires 1) not knowing things, and then 2) reading works from people that know those things until I know those things too. So, with my faculty position looming on the horizon, and with it the amorphous and exciting batch of new responsibilities that role brings, I feel the need to read some articles/books on best practices.

I sent a tweet out fishing for advice

and received a lot of feedback! Thanks twittersphere! I made a Google doc of the recommendations, and I hope it is useful for other new faculty. Please add more!

The editable Google doc is here: Books for new faculty.

UPDATE: Books for new faculty part 2 (and new students part 1) here.

A little while ago I was looking for an active way to teach about the evolutionary dynamics occurring within each of us. So, finding the perfect excuse to learn some shiny, I built a simulation of an evolving stem cell niche that students can control—a fun evolutionary game to play!

Give it a shot! Either on the shiny website while my account can support the simulation or try it yourself straight from the github source page.

The goal here is to understand how inherently “random” dynamics—cells are chosen to divide based on a die roll and chosen to leave the system based on the flip of a coin—can manifest in outcomes that are predictable. For instance, it turns out that neutral variants, i.e., mutations that do not affect the relative division rate of cells, have a knowable probability of “fixing” in the population (taking over) and consequently a knowable probability of going extinct. You can adjust the starting size of the mutant population or the starting size of the entire population and see how this changes the probability of fixation.

You can also adjust the relative division rate of “mutant” cells, and see how this changes probability that the mutant lineage takes over the system. This difference in fixation probability is the intensity by which the mutant is naturally selected to survive.

In other words, if you have information about the actual rate of fixation of variants, and the expected rate of fixation of the variants if they were neutral with respect to selection, you can calculate the differential intensity of selection for these variants, and you can understand which variants give the largest boost to cellular division and survival. These are the same sort of tools we use to understand which molecular variants are driving cancers! And, of course, the evolutionary dynamics occurring in the small populations that constitute our bodies are hugely important!

I built this simulation hoping that others can use it in their classrooms as well  Please let me know if you think of any ways to improve the simulation, the code, or anything at all!

72 days from reading this, more than half of the cells in your body will be completely different cells than the cells in your body today. 

Don’t ever let someone tell you “people never change.”

People are always changing. Sitting there, reading this, you—the mass of writhing, wiggling, cooperating, and competing cells that constitute your corporeal self—are changing. You are in flux. Millions of your cells have just died, and millions have just been born!

In talks, and on this blog, and in my papers, I often discuss this personal turnover because it leads to interesting biological questions. But I always paint this picture in the light of specific tissues. A recent conversation had me wondering—what about the entire body? What percent of our total cell number are different after a day? A week? A month?

Time for some more back of the envelope calculations!

Let’s say that 25 trillion (25 with 12 zeros after it, 25,000,000,000,000!) out of the 30 trillion of the cells in your body are red blood cells, as estimated by Sender et al. (2016). These red blood cells have an average lifetime—marking the time they are born until they are eventually recycled—of 120 days.  This turnover is a continuous process that keeps our blood fresh and functional each day.

So, every day, about of the of our cells are renewed, or , i.e. at least of our total cells are renewed daily! I stress at least because this estimate just includes the turnover of our red blood cells… our skin, our intestinal epithelium, and many other tissues that account for the 5 trillion cells that we didn’t include in the above calculation are continually renewed as well.

How long until half of the 30 trillion cells in your body are different from today? Again, just thinking about red blood cells, we need to calculate how long 15 trillion of these cells take to be recycled. 15 trillion is of the total 25 trillion blood cells, and if the full batch of blood cells is renewed every days, this means that of the blood cells will be renewed in days!

I recently read a bit of Why Evolution is True by Jerry Coyne and stumbled upon a fun fact I needed to dig into.

Hundreds of millions of years ago, there were corals, just like today. And, just like today, they grew by depositing a ring of calcium carbonate onto their outer skeleton every day*—similar to the growth patterns in the trunk of a tree. When you look at these growth patterns in living corals, taking into account changes of deposition with seasons, you can see annual growth patterns and, as one might expect, about 365 daily rings per year. When you look at fossil corals from 400 million years ago you see over 400 daily rings per year!

Scientists have long predicted that the rotation of the Earth must be slowing down due to tidal friction—the motion of the tides have been dampening our angular momentum (it’s stolen by the moon!). Not by much, about 1 second gets added to the day every 50,000 years. But, over millions and millions of years, these seconds add up. 600 million years ago, a day was 21 hours long, and over 410 of these days elapsed before the Earth could complete its annual journey around our sun. In 1963 Prof. John W. Wells used a biological calendar—fossilized corals—to corroborate astronomical predictions about our lengthening days.

Anyway, it was the perfect storm of fun facts. The days are getting longer, coral living 400 million years ago experienced over 400 days a year, and we can see this in a biological record.

P.S. Modern corals are still keeping a record. Using “coral chronometers” we have a record of variations in temperature, cloudiness, and even nuclear activity (some bands in corals coinciding with nuclear tests are radioactive). Maybe 400 million years from now somebody (something?) will find corals from today and see the impact of the human era. At least 400 million years from now the postdoc doing this research will have ~27 hours in a day to write up the results.

* I also just read Jurassic Park, hence the Mr. DNA adaptation.

Begüm gave me a GoPro camera as a gift for submitting my dissertation (because I live such an #extreme life). We put it to good use and made a timelapse of our travels from Florida to New England! I think it came out pretty cool, check it out:

There is no audio—I recommend choosing your own song. On a related note, we had our playlist on shuffle and “New York State of Mind” by Billy Joel came on right when we reached the Verrazano bridge. It was a pretty powerful homecoming moment for this Long Island kid. And then it started raining pizza and bagels (ok, I made that part up.)

Our route:

Gainesville, FL –> New Orleans, LA –> Atlanta, GA –> Savannah, GA –> Raleigh, NC –> Philadelphia, PA –> Long Island, NY –> New Haven, CT –> Boston, MA

Many many thanks to our friends Scott, Dorian, Kevin, and Rich and Amber who hosted us along our journey. During graduate school we always daydreamed about taking a roadtrip after we finished up, and you really made it possible for us.

Begüm is the family photographer, so I don’t have many images to share. But, here are a few scenes from the road.

All packed up saying goodbye to our little apartment and our little garden.

Watching the ships roll by in New Orleans.

Meeting some new friends in Atlanta.

And some old friends.

Watching the dolphins, Savannah, GA

Becky helping us unpack, Long Island, NY

We hope to fill out that map with more trips in the near future! Next up: Cannataros head West (Geneseo, Niagara Falls, maybe some Canada adventures).

p.s. Dear Upstate NY friends: We are willing to trade delicious homebrew in exchange for lodging.

If we’ve ever talked shop, or you’ve seen some of my slides, you know I like talking about what constitutes “you.” Especially as it pertains to the ever-changing, continually in flux, nature of our cells.

Which is why my friend Anthony knew I would enjoy this video (thanks for forwarding it along!) I particularly enjoy their description of our non-static essence. Don’t let anyone ever tell you that “people never change!” I could talk about this for hours, and I’ve devoted much of my research to the subject, but I’ll let the videos take it away– they do a really beautiful job.

What are you?

I also really enjoyed their Ship of Theseus explanation!

When you are done chewing on that, I hope you will check these out–

You are two.

Genetic Engineering Will Change Everything Forever – CRISPR.

It’s an exciting time to be a biologist!

Dear friends,

It has been a while, I know. My tweets, posts, and overall online ponderings have dwindled this last year. But a lot has happened! I finished up my Ph.D. research, wrote up and successfully defended my dissertation, graduated from the University of Florida, and started as a postdoc at Yale University.

Featured: my wife, Begüm, UF Ph.D. grad ’15, aerospace engineering.

Now that we are (almost) all moved and the work/life balance is settling into somewhat normalcy I have had some time to reflect on my graduate school experience. I was thinking about writing a post on “what I wish I learned sooner” or “what I would tell myself 6 years ago”, but after giving it some thought I realized that I wouldn’t change anything about my path through grad school. Every lesson that I learned “the hard way” was a necessary struggle. Nevertheless, I’d like to share some of the lessons I learned the hard way, and maybe if you are reading this as a first year graduate student you can keep them in mind as you forge your own path and learn your own lessons.  So, off the top of my head, things I learned the hard way, in no particular order:

• Treat grad school like a 9-5 job.

  When I first got to graduate school the postdoc in our lab suggested I treat graduate school like a 9-5 job. As in, have the discipline to go work every workday, even if you do not have any obvious obligations. It was easy to not do this, since my advisor didn’t set any rules about being in the lab, my work didn’t revolve around keeping lab animals happy, and I only taught a few days a week. Plus, I just moved to Florida and was living in a small city with 50,000 other young adults…

  Grad school is a lot of work. Before you know it there will be a bunch of obligations to tend to, many of which are completely new. Teaching, grading, your own classes and homework and exams, and, hopefully, your own batch of new research. Not to mention the little things nobody talks about, like spending an entire week trying to figure out how to use a program to make one figure that you never even use.

  All I’m saying is that you will thank yourself later if you build work habits and discipline early, even if you do not have any established research yet. (Thanks for the advice, April!)

  That all being said, make sure you figure out when it is appropriate to close your email, turn off your phone, unplug from the internet, and take care of your mental and physical wellbeing.

• Find where you work best.

  The lab wasn’t always the best place to do work. Maybe your labmates are doing intensive noisy work. Maybe people are holding office hours. Maybe the building is old and heavily used and in Florida and it fills up with cockroaches after 4pm. I went through many phases in grad school with ideas about where I worked best. In the beginning, it was the library, in the middle it was a coffee shop, and at the end it was in the lab and at home (and at the VERY end it was everywhere, all the time). And, if you have a mental block, it might be best to get up and go for a long walk and change your environment.

• Comment your code.

  I know this is the first thing you learn as a new coder/programmer. I know the (good) code shared on stackexchange is beautifully commented. I know it is SO obvious that this is the best practice. But, it is easy to get lazy. Especially when you don’t think anyone else will ever see that particular batch of code.

  I didn’t know how easy it is to completely forget what I just did two weeks ago. Maybe you are meeting with your advisor, and she suggests trying to do X,Y, and Z, and you say “Great idea! I actually did that two weeks ago!” and then you go to pull up your code (if you can find it, that’s another lesson I learned the hard way) and it looks like someone else wrote it.

  Trust me, even if you are writing one line of code to do something fairly trivial, add a comment saying what you are doing, why you are doing it, and how it works (in English)… and how it fits in with the rest. Maybe it’ll take an extra minute that feels wasted, but it is better than actually wasting 15 minutes at some later point trying to decipher your mess. You will thank yourself when a reviewer comments on results you generated 8 months ago and you need to rerun everything! On that note, I found using github extremely useful during the latter part of my Ph.D. work. Especially when I had multiple projects going on at the same time and I needed to jump between them and remember exactly where I left off.

• Create figures directly from your code.

  Perhaps you have no formal training when it comes to coding. Perhaps you learned everything in a nice GUI and you can save figures by clicking “Save Figure As…” (I learned in RStudio). Perhaps you have gone years coding and generating results and saving them to random folders throughout your computer and it has never been a problem. You finally submit the first chapter of your dissertation for peer-review, and:

  “Please resubmit Figure 2 at 600 DPI.” Ok, no problem, you think: where is the code I used to generate the original figure 8 months ago? Where did I even save the original PDF?!

  Better way: directly save your figures within your code. This is fairly simple in R, and I do something like this:

>#####

>#Alright, time to make that neat figure from the data generated from the code immediately above (descriptive comments throughout!)

>#Mouse, tumorigenesis incidence, things I can Ctrl+F here

>#####

>plot.count <- 0    #initialize some counter

 

>plot.count <- plot.count+1    #this counter goes up 1 time every time you run the code to generate a new figure in the same folder. This way, you can try a bunch of dimensions/font sizes/etc. in a row and pick which looks the best

>png(height=12,width=8,unit=”in”,res=300, file=paste(“C:/Users/Vincent/Desktop/aging/evo_tradeoff_figures/”,”mouse_combined_wline_exitseminar”,plot.count,”.png”,sep=””))  #making a png image, and now we know the exact dimensions, resolution, and where it was saved. Note that the counter is within the (descriptive) filename.

> #CODE TO OUTPUT THE PLOTS GOES HERE.

>dev.off()   #Turn off the “I’m outputting images to a file now” signal.

There, now you can look back to your code and see how the figure was made, where it was saved, and easily redo it!

• Do not use powerpoint to create (manuscript) figures.

  Powerpoint is great. Over the last few years I have become a powerpoint whizkid. I love it. But, I’ve come to realize that it isn’t the ideal choice when making (professional) figures. It IS great for a quick and dirty manipulation of something for a presentation. It is great because it is easy. But, when you are stitching a bunch of images together, over a background, with a specific DPI requirement, and dozens of layers, you might need something a little less easy. That’s where GIMP (GNU Image Manipulation Program) came into play. It took some getting used to, but trust me, it’s worth it. (Just like learning LaTeX over Word.)

  https://www.gimp.org/

• Set up alerts to find new papers.

  Whether it is a specific journal’s Table of Contents or something like Google Scholar Alerts, do yourself a favor and set up some automated email service that alerts you to new papers in your field and interests. Even if you just take note of authors and titles and abstracts, it’ll keep you at the edge of your field, and you never know who you will meet at a conference!

• Join listservs

  The messages I received through Evoldir alerted me to numerous conferences and postdoc opportunities. Worth deleting a dozen or so messages every morning.

• Join Twitter (maintain a semi-professional social media presence)

  I once heard someone describe their persona on twitter akin to if they were at a beer and wine social at a conference. Professional, but also fun and somewhat personal. Through Twitter I have shared my interests, met many researchers in my field, and have had my research (e.g. slides at a conference) sent out to tens of thousands of people. It’s great! Plus, you never know, maybe your university will even pick up on your story and want to share it with the world.

  

  

  

• Give talks!!

  My graduate career has been punctuated by the talks I’ve given. Nothing helps you solidify your thoughts and results like having to announce them to a group of colleagues. Invite the meanest, most critical, and intimidating professors you can think of, because they will probably have the best feedback. Invite mathematicians if you describe math. If you have the opportunity to present in an informal setting, then share new half-baked ideas with colleagues. I can’t stress this enough, sharing your work with the field is one of the most challenging and rewarding parts of graduate school.

If you went through this journey and want to share some things you learned the hard way, let me know! I’ll update if I think of anything else.