COVID19 science and links

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 .

Screen Shot 2020-03-10 at 10.04.08 AM

Screenshot of COVID19 relatedness from 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:

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: I have a public github repository here 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 a markdown 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.

Books for new faculty (part 2) and also for new students (part 1)

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

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

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!

Books for new faculty

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.

An interactive evolutionary game

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.

Screen Shot 2019-06-20 at 12.01.10 PM

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.

Screen Shot 2019-06-20 at 11.59.23 AM

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.

Screen Shot 2019-06-20 at 12.06.26 PM.png

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!



People always change

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 \frac{1}{120} of the 84\% of our cells are renewed, or \frac{1}{120} \times \frac{84}{100} = 0.007 , i.e. at least 0.7\% 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 \frac{15}{25} = 0.6 = 60\% of the total 25 trillion blood cells, and if the full batch of blood cells is renewed every 120 days, this means that 60\% of the blood cells will be renewed in 0.6 \times 120 = 72 days!



A biological calendar

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.

Bill Nye the Science Guy meets Vin Can the Science Man

OK, so I need to work on my stage name.

Back in May 2017, I tweeted at Bill Nye:

And it turns out that Bill, and the great writers and producers and everyone else behind Bill Nye Saves the World, were paying attention. Shortly after the tweet I was contacted by a producer of the show and asked if I would like to come on and give a demonstration about the evolution of “super-bugs”, i.e. antibiotic-resistant bacteria.

An opportunity for science outreach involving Bill Nye? Yes, please.

In this post, I first want to talk about the science in my 5 minutes (at the end of Season 2 Episode 3 of Bill Nye Saves the World). Then, I’ll touch on the experience of being on the show.

The Science

I wanted to convey three things in my demo:

  1. Antibiotics work really well!
  2. So does natural selection. In the presence of an antibiotic, bacteria resistant to that antibiotic survive and proliferate more than non-resistant bacteria, leading to the spread of the information conferring that resistance (i.e. the evolution of “super-bugs”).
  3. And that’s why it is important to be judicious about antibiotic use.

all while conveying how scientists can use models of reality to study biology.

So, for my demo I created a model of the evolutionary dynamics of bacterial strains within a person. In this model, bacteria either replicate or die—similar to a common mathematical model used to study evolutionary dynamics called a “birth-death process”. If there is more birth than death, the bacteria grow too big and overflow from the host—the infection spreads to other hosts. If there is more death than birth (as in a typical situation where the immune system does a good job), the bacteria die off —the infection is cleared.

What makes this a model of evolution is that we can introduce two different bacterial strains into the model and observe how the relative abundance of these two strains change within the total bacterial population over time. Let’s say one strain has a mutation in their genome that makes them resistant to antibiotics, and the other strain is still susceptible to antibiotics.

Let’s also assume that the host’s immune system is compromised, all strains are growing more than they are dying. The person goes to their physician, and gets some antibiotics that decrease the birth rate of only the susceptible strain. Growth of the susceptible strain is stopped, but the resistant strain grows and grows, and when the model “overflows” it is the resistant strain that spreads to other hosts.

By continually providing selective pressures favoring resistance, we drive susceptible strains to extinction. As the model suggests, we would expect the spread of antibiotic resistant bacteria to be especially prevalent in areas that have a high concentration of individuals with compromised immune systems that take antibiotics, such as hospitals and nursing homes.

But, there is hope! Many of the mechanisms of resistance are actually costly to bacteria when antibiotics are not present. It may be possible to reverse many of the mechanisms of resistance (select for non-resistant strains) by being extremely judicious about when to apply antibiotics. The original focus of the demo was on how to reverse resistance through exploiting this cost of resistance, however due to time constraints I refocused on the emergence of resistance.

My Experience

Everything was awesome. I had no idea just how much went on behind the scenes to get a show produced. From the props people helping with my demo, to the writers and producers working around the clock anticipating every little thing that will happen. Everyone really cared about being true to the science and explaining the information in an accessible and exciting way. Especially Bill Nye, who was extremely genuine and kind throughout the whole experience. I’m very grateful for the opportunity to help #savetheworld!




For those who arrived at my blog curious about my current research: I use mathematical models and simulations to investigate how tumors evolve from our tissues, how evolution has structured our tissues to minimize the risk of cancer, the effects of mutations in growing tumors, and how cancers evolve resistance to chemotherapy. Relating to pathogen evolution, during graduate school, I was part of a team that used mathematical models to study the evolutionary dynamics of pathogens and their hosts.

Dark selection from spatial cytokine signaling networks — Theory, Evolution, and Games Group

Check out a post I wrote over at the Theory, Evolution, and Games Group blog on some of our work at the 2016 Integrated Mathematical Oncology (IMO) Workshop! The link for that post is at the bottom of this post.

It details a really neat model we created to interrogate a system of cytokine signaling and cancer treatment. For those unfamiliar with the IMO Workshop/competition, five teams of a dozen or so researchers, all from different backgrounds, are formed at the beginning of the week, and quickly decide on an interesting research problem they can tackle. Each team has a few physicians and scientists stationed at the Moffitt Cancer Center, where the competition is held, that act as mentors. The groups spend the four days working and researching and planning ahead, and on the last day they all present their completed and proposed work. Oh, did I mention that $50,000 of future funding is on the line? The winning team gets the $$ to complete their proposed research.

This sets the stage for an awesome week-long hackathon, where longer and longer workdays culminate in an inevitable all-nighter as mathematicians and computational biologists and physicians and new colleagues perfect their models and presentations.

So, there we were, 35 hours or so away from the final presentation, when we all decided we needed a spatially-explicit model of cytokine diffusion and cell response. I had created spatially-explicit simulations of cell turnover before, so I volunteered to lead the analysis. And, like the scientist in an action movie rushing to find the vaccine for the zombie virus before the meteor strikes (or something), I worked overnight in my hotel room, and all the next day, and delivered this video and results right before the final presentation:

(For more information on what the video is showing, check out the post linked below or our preprint.)

It was only 2 slides worth of work within our whole presentation, just to give you a sense of how much everyone in the group accomplished during the week. But it was actually a ton of fun rushing to get everything together and connected. And, we won the competition!


Greetings, Theory, Evolution, and Games Group! It’s a pleasure to be on the other side of the keyboard today. Many thanks to Artem for the invite to write about some of our recent work and the opportunity to introduce myself via this post. I do a bit of blogging of my own over at […]

via Dark selection from spatial cytokine signaling networks — Theory, Evolution, and Games Group

How much energy is in a thought?

Sometime during the last months of grad school I was in the office late, polishing off one too many coffees, and dipping into my emergency ramen noodle stores. I was searching for that elusive (and perhaps illusory) moment of clarity that, one hopes, arrives to propel a manuscript forward. But, the long hours and coffee caused my mind to wander into distant realms of science. I had just finished teaching about neurons and action potentials and brain activity in my physiology class (100 billion neurons, forming 100 trillion neural connections—more connections than stars in our galaxy—sparking up right now allowing you to think this!) and I had a cool thought:

I am converting these cheap noodles directly into science and new insight. I am a biochemical machine that converts packs of 10 cent fake noodles into knowledge.

And then, the natural follow-up: at what rate? What is the cost of a thought? How many noodles does my brain burn to construct a statement? A paper? A dissertation?

Now that I do not have a dissertation submission deadline looming, I have some time to explore these thoughts—thankfully while burning some higher-grade fuel than emergency ramen! Warning: the calculations that follow are extremely ‘back of the envelope,’ and should be taken with a heaping helping of salt and skepticism. This is just a fun exploration.

How much energy is burned in a thought?

First, let’s gather some parameters. How much energy does the brain use? The short answer is: an incredible amount. Despite only accounting for 2% of the body’s weight, the brain uses 20% of the body’s energy (that figure is for an adult, in newborns it is 44%!!) The brain uses 2–3 times the amount of energy that the heart uses.

[Aside: the brain is extremely efficient at what it does—processing information using orders of magnitude less energy than the best supercomputers.]

So, let’s say that the brain uses 20% of the body’s basal metabolic rate, and the basal metabolic rate is 1500 kcal/day. That means the brain uses about 300 kcal/day, or 0.0035 kcal/second.

The next question is: what is a thought? How much time does one take, and what proportion of the brain’s energy is devoted to “thinking”? I don’t know! But, does anyone know? I don’t know that either. Since it is my blog, I am at liberty to define a thought. Let’s say, for the sake of argument (and feel free to argue in the comments) 100% of the brain’s energy is required for “a thought,” and all thoughts are created equal. And let’s also say that a thought is a statement, and that it takes as much time as one would take to think or read a sentence. For instance, here is a thought:

“Wow, I am thinking this thought about thinking; this is one of the things that hydrogen atoms do given 13.82 billion years of cosmic evolution, and it’s super cool.”

How long did it take to think that specific (extended) thought?  More than a couple of seconds, less than 10? Let’s say a substantial thought takes 5 seconds. At 0.0035 kcal/second, that’s about 0.02 kcal/thought!

So, how many ramen noodles are burned for a thought? At 400 kcal per block, and 150 noodles per block, we have 2.67 kcal per noodle. Assuming the average noodle is 33 cm long, we find that there are 0.08 kcal/cm of noodle—and every thought burns about 0.25 cm of ramen noodle! Your brain is incredibly efficient—no wonder that future AI are always super jealous and vindictive in sci-fi movies.

Now we can readily convert thinking-time into calories, and content creators can register their influence in energy. For instance, if 100 people read this blog post, consuming 5 minutes of calories thinking through the content, then about 100 calories would be burned on my words. 400 people and an entire block of ramen has been consumed by my words.

I wonder how much ramen has been burned by Shakespeare?

Spider Sunday, Puerto Rico edition

We had an amazing time recharging our science batteries and exploring the island of Puerto Rico for a few days this August:

We spent much of the vacation out and about in Nature, meeting new (to us) birds and frogs and lizards, and some really cool plants.


One surprising find was seeing our old friend, the golden orb weaver. We even saw one outside the Cueva Ventana that had succumb to a fungal infection of some sort:


Dead Golden orb-weaver. Check out the fungus growing out of the leg joints.

I showed this image to a mycologist friend and they thought the fungus may be from the genus Beauveria. In all of my years living in orb-weaver infested Gainesville FL, I have never seen a dead banana spider!  Had to share!