This infographic has gone viral across the past few days, and for a very good reason. Many PhD candidates in biology aspire to have the life of their mentor in graduate school. I will even admit that it seems like utopia except for the time away from the bench. I hope to never be that hands off. Three things are striking to me on this infographic derived from an NIH report published in 2012 on enrollment, retention, and short- and long-term success in academia. First, the high dropout rate of 37% across the average length of time that it takes to get a PhD in biology which is about 7 years is higher than other professional degree programs: law school, business school, and med school. Although this can easily be explained by the lack of financial commitment. Most PhD candidates are paid enough to not have to take out loans or find another job whereas those in other professional degree programs feel the need to bite the bullet if they are already $100k + into paying for their program. Second, PhD candidates say they want to get a PhD because they want to be in a faculty position. The NIH portfolio reveals, however, that a tenured faculty position is a rare gem, reserved only for 8%. This statistic is likely lower for women and minorities because let’s face it, even in 2014 academia still greatly struggles with recruiting more than just frat boys. Lastly, I was shocked to learn that postdocs really aren’t as mistreated as I previously thought. The average length of one is 4 years and only about 30% do a second postdoc. Maybe I am hanging out with the wrong crowd. At any rate, this infographic emphasizes the importance of planning ahead in the short- and long-term by means of having a Career Development Plan. Fill one of these out and post it to your bulletin board. It is helpful, trust me.
For regular readers, you may recall that I have been involved with a multi-part study of elite Crossfitters. The research enterprise continued last week when I did a very challenging (and my first) test of VO2 max. One would think that elite Crossfitters have cardiac and respiratory output that rivals that of elite endurance athletes due to the need to go hard and to continually generate power for extended periods of time. Turns out, we are far from these folks.
The visit to Kennesaw State University began with a body scan which provided measures of bone density, free fat mass (i.e. muscle), adipose density, and limb dominance. It is no surprise that my bone density is nearly off the charts. I have been an athlete since the age of 3 and have always engaged in sports that create significant impact on bones and joints and are known for building bone density, gymnastics, track and field, and strength and conditioning with barbells.
While my percent body fat corroborated with a previous assessment under a different machine–it is around 14% which is normal for competitive athletes–I was surprised to learn that I am slightly more right dominant in regards to bone density and free fatty mass in my limbs. Although I write with my right hand, I am also very comfortable using my left for things that require fine motor control and movement like small animal surgeries. Further, I have always been a leftie in gymnastics via mounting, tumbling, and dismounting with my left leg in front, in track and field via taking off the runway for long jump and pole vault on my left leg, sprinting from the starting blocks with my left leg in front, and preferring to hurdle with my left leg. In crossfit, I land a jerk with my left leg in front.
After these sedentary assessments, it was to the treadmill. The protocol was very similar to our Crossfit training regimen of short bursts of activity with equal time of rest with an ascent in weight; in this case, the weight was replaced with an increase on the grade of the treadmill. Once I found a comfortable and slightly challenging pace of 8:30 per mile, I ran for two minutes. Each minute, the incline of the treadmill increased by 2.0. This means that by the end of the first two minutes, the incline went from 0.0 to 2.0. After this two minutes, I was allowed to rest for one minute during which time my blood lactate was collected. This went on and on until I failed. Most of the test subjects allegedly made it to 12-15 minutes. I went for almost 22 minutes. I was completely delirious by the end though.
Until next time….
I have been idle the past week because I have been in Galveston, TX for a secret society meeting of sleep researchers. It wasn’t really secret, but it was an invite only conference limited to less than 200 participants worldwide. The focus of the meeting was the neural mechanisms of sleep, namely those studied in animal models. As I expected, there was a central focus on optogenetics and DREAADS, recently re-named chemogenetics. Both of these methodologies have emerged about a decade ago, but have really taken off in popularity within the past two years. Basically, optogenetics and chemogenetics utilize light (opto) or drugs (chemo) to activate certain populations of neurons in specific brain regions through the assistance of transfected bacterial and viral factors.
The trip began with fulfilling every space nerd’s dream: visiting NASA and the Mission Control Center where all of the Apollo missions launched from, including Apollo 11. This room is the most famous historical landmark in the world, not just in the US. The technology of the room reminded me of human sleep laboratories from the 60-80s. The current Mission Control Center is below this room, but we were restricted access. Regardless, we also got to see the Saturn 5 rocket. Words can’t describe this monstrosity.
After a trip to NASA and some of the best Tex Mex that I ever had, we traveled to the Hotel Galvez–the conference locale–which is a beautifully maintained resort built in 1911! It was stunning and the ambiance reflected the decade that it was built.
The conference schedule was very intense, which is characteristic for a Gordon. Each day began at 9 AM, broke off for lunch around 12:30, resumed around 4 PM with posters, broke for dinner, re-started at 7 PM and ended with a nightly social at the hotel bar around 9:30 that usually lasted until 3 AM…..yes, sleep researchers do not by any means practice what we preach. For as much as I wanted to hang until the wee hours of the morning, I had to limit myself due to being in the middle of my Crossfit season.
As for the science, here are some buzzwords and key findings presented in order of salience (to me):
1. Corollary discharge: Dr. Mark Blumberg of Iowa presented evidence that twitching–a characteristic of REM sleep, particularly in neonates–is necessary for proper brain development. In describing the mechanisms of action of twitching, corollary discharge is this idea that the signal can bypass centers of the brain regulating consciousness. This is the reason why you can’t tickle yourself. Dr. Blumberg has studied twitching in neonatal rodents using a series of illuminated electrodes to identify frequencies and movement patterns.
2. The thalamus: The thalamus has been overlooked in recent years of sleep research. This is surprising considering that it is the major gateway for sensory and neural processing that underlie most biological processes. Luckily, Dr. Michael Halaasa from NYU studies the role of thalamic circuitry in memory and learning and the EEG waveforms, namely spindles, that drive these processes.
3. The Aplysia: I am really fascinated by research conducted in this neurologically simple invertebrate, particularly since one of the most renowned neuroscientists of the century–Dr. Eric Kandel, a Nobel Laureate– used the Aplyasia to study circuits of learning and memory. At this meeting, Lisa Lyons from Florida State studies learning and memory processes in the Aplyasia to examine how they are impacted by sleep loss coupled with circadian disruption. Turns out, they suffer just as much as us.
4. Astrocytes: There was a significant focus on adenosine–a neurochemical marker of sleep homeostasis released from astrocytes–and its relation to physiological sleepiness, inflammation, and memory. Dr. Jason Gerstner of UPENN also studies fatty acid metabolism in astrocytes.
5. Local sleep: This has been a salient topic in the sleep field for a few years now. The idea is that certain brain areas require more sleep–neuronal “offlining”–than others and that this phenomenon is directly proportional to the extent of use. So, you can imagine the the motor centers of athletes are in greater need of local sleep compared with those of sedentary folks.
There were plenty of other great posters and talks at the meeting, but these were certainly in my top five. Overall, I was very impressed with the quality of work and presentation at Gordon. It is as if there was a try-out for being a speaker because every talk was THAT good. This rarely happens at any scientific conference.
Great conference overall.
More from my studious husband….
Here’s another contribution to my analyses of the CrossFit Open competition, and is continued from here, where I looked broadly at maximum and minimum placings among Open competitors, and here, where I examined the frequency that athletes finished within the top 60 of a given Open workout and how that related to qualification for Regionals.
This particular post is an extention of my brief analysis examining probabilities of Regional qualification with top 50 finishes during the Open.
This post attempts to address (2): your chances of qualifying for Regionals with a particularly high Open placing.
I’m going to refer to “maximum placing” again, which seemed to cause some confusion in my past posts. Maximum is highest absolute value. For instance, 398th is a higher place than 2nd place. So, “maximum” is “bad” if you’re interested in competing.
I’ve got plots to present, and I will summarize them in the last paragraph of this post.
Past probabilities of qualifying based on maximum and minimum placings
So you’re interested in qualifying for Regionals, but 14.3 is a culmination of all of your weaknesses: triple-under, backflip, muscle-ups, while holding a perfect D-flat major. You compete, and finish as expect, but not to your liking. Do you still have a chance at qualifying?
Fig. 1: Regional qualification (1 = Yes, 0 = No) of athletes with increasing maximum placings in a given open. The line represents a generalized linear model fitted to binomial qualification data, and predicted with maximum placings. Closed points are empirical probabilities with standard error.
Figure 1 is similar to what I presented in my previous post, but the x-axis has been replaced with Maximum Placing – the highest place value (remember, high is ‘bad’) an athlete received during a given Open competition.
Each little, vertical line represents one athlete, during one Open competition (1800 total), and it is placed on the bottom (y = 0) for non-regional qualifiers, and on the top (y = 1) for the Regional qualifiers. Where each line is along the x-axis represents her maximum placing of the five workouts in a given Open.
Again, the line is what is interesting, and the rapid change is what is more interesting. The line lets us estimate the chances of an individual qualifying for regionals, given a particular place in an Open workout – in this case, it’s the maximum place scored. As you move from a low maximum (<50th) to about 300th place, qualifying athletes drop dramatically. Let’s zoom in:
Fig. 2: A modified scaling of Figure 1 – the x-axis has been limited to < 350.
If we again follow the closed circles, which represent empirical probabilities, athletes with a maximum placing below 50, have a 100% chance of qualifying, but those chances start dropping off quickly:
- 97% with maximums below 100th
- 57% with maximums below 150th
- 18% with maximums below 200th,
- 4% with maximums below 250th, and
- 0.09 % by the time we reach a maximum of 300th place.
This is exactly what I found in my first post – get a score above 300th place and you’ll be breaking records if you qualify for Regionals. I need to emphasize the limits of my dataset. These ‘probabilities’ are not actually probabilities (more accurately, they are estimates of the contribution of maximum placing to regional qualification taken from fitted models to past data), and they only apply if you happen to be a women, in one of the five regions listed in the first post… during Open years 2012 and 2013.
So, take these data with a grain of salt. In fact, take them only as encouragement, and a push to do better next time. The data simply show what has occurred, and one of the mantras of CrossFit is to always push harder and surprise yourself. If you finish 301st in 14.2, make me recalculate my estimates and make new plots. (is that motivating to anyone?)
The Reebok Crossfit Open simultaneously brings out the best and worst in me. I enjoy the physiological onslaught of pain and fatigue a few minutes into the workout coupled with the post-workout euphoria. I also enjoy the friendly competition in the gym or seeing one member of the community achieve a new goal, like a double-under, barbell weight, or muscle-up. However, I also become physiologically (and psychologically) stressed over the anticipation of each week’s workout and my obsession with planning the perfect workout in my head even if I have already attempted it once and got a decent score. You’d think that I would have adapted by now being a four-year veteran to the Crossfit Open. Nope. However, being a veteran also means that I have been better able to manage my recovery better, especially now that I am almost in a new decade.
I may sound like a whiner, but Crossfit at the age of 26 is different than the age of 29. I used to be able to have some alcohol and marginal sleep across a weekend and be able to train and perform fine the following week. This is no longer the case. I remember having celebratory beers with other gym members after each open workout, especially if I had done better. Nope, my body can no longer adapt to that.
Here are some foods and drinks that I have found helpful to recover during the Open and heavy waves of training. I have adopted most of these this year, and I will say that my rationale for eating and drinking these things are backed by empirical evidence from papers that I have read for my own research; I study skeletal muscle physiology and what impact it has on sleep.
1. Get your niacin: Because the Open workouts are metabolically demanding via complete exhaustion of glucose load in the muscles coupled with many transitions from anaerobic to aerobic respiration, you need to be replacing a major component of glucose-burning pathways; nicotinamide adenine dinucleotide (NAD+). Niacin is a constituent of NAD+. Here is the Krebs cycle? Remember this. Look at how many times NAD+ picks up a hydrogen–gets reduced, for biochemical nerds–and ist then shuttled to the mitochondria to produce more energy in the form of ATP. Obviously, the availability of ATP is a rate-limiting factor between excelling and burning out.
Which foods are rich in niacin? Peanuts, peas, beef, chicken, and fish. If you are like me, head to Five Guys for a bunless burger after each Open wod. You can get two beef patties plus lots of peanuts to snack on.
2. Tart cherry juice and coconut water: Any competitive athlete needs simple sugar to stay afloat. Crossfit training taxes metabolic reserves of the muscle, particularly that of glucose. For athletes like me who do multiple workouts in a day, you need to replenish your glycogen reserves quickly. Sure, fats and protein are great for you too, but nothing gives you a quick boost prior to one of many wods in a day than some simple sugar. I like juices because they replenish glucose reserves quickly and particularly for coconut water, you get replenishment of electrolytes–sodium, potassium, calcium, magnesium–as well. Another great juice to consume at night is tart cherry juice. Tart cherry juice is pretty high in sugar–a single 8 oz serving has 150 calories all derived from sugar–but it is also high in melatonin and antioxidants. This is only if you get 100% tart cherry juice and not some diluted version. Not everyone is sensitive to over-the-counter melatonin –or sources not derived from direct production and release from the pineal gland at the base of the brain–but if anything, the extra sugar in tart cherry juice will help you recover quicker.
Along the lines of tart cherry juice and coconut water, bananas will also do the trick. They are high in electrolytes and they have a high glycemic index, raising your blood sugar quickly.
3. Fish oil: Seafood provides an excellent source of omega-3′s which are nature’s own anti-inflammatories. Obviously, Open workouts are designed to wreak havoc on the ligaments, tendons, muscles, nerves, etc. A little extra anti-inflammatory protection can go a long way.
Well, I’m not a nutritionist for a living. I’m just passing along recs based on what I know about the human body and its ability to adapt to high intensity training. Good luck!
Here is more from the brilliant, statistical mind of my husband.
Here are a couple of histograms illustrating the frequency of top 60 placings for competitors in a given year and region. So, an athelete, say, Emily Bridgers, completes 5 workouts during an Open. How often does she place in the top 60 during that Open event? 5 of 5 times. I wanted to consider this for 1800 athletes, 600 of which finished in the top 60 over two years and five regions.
So, how many athletes did the same as Emily, finishing 5 of 5 workouts in the top 60? 150 (Fig. 3). Yes, there are duplicates – Emily did this (5 for 5) both years, and it’s counted as two ‘separate’ athletes in the estimate. There are other criticisms one can provide, and maybe I’ll consider them… but here’s a summary of the data I’m talking about:
Fig. 3: Frequency of placings in the top 60 for the top 60 Open finishers.
Fig. 4: Frequency of placings in the top 60 for athletes finishing from 60 to 180 during the Open.
I split the rankings into two groups: the Top 60 finishers (Fig. 3) and athletes that finished between 61st and 180th (Fig. 4). Within the top 60, the majority of athletes placed within the top 60 during five open workouts at least 3 times – about 450 of 600 did this. About 100 athletes finishing in the top 60 placed within the top 60 twice during a given Open, and a handful (~45) even did this once. In fact, there were two athletes that never finished within the top 60, and finished with an overall placing of 56th and 58th… Why? Because their placings were consistently between 62 and 140. They never had a bad workout.
That said, look at Fig. 4. There were nearly 400 athletes that placed in the top 60 during an Open, but placed overall between 61st and 180th. 400 of 1200 athletes. So the Open works both ways, and this is supported by Fig. 2 (look at the right side of the graph… see how many points there are really close to the x-axis) and Fig 4., one good work out will not guarantee you a spot at Regionals, and one bad one won’t guarantee you’re knocked out of the running.
These results suggest that Regional qualification is weighted heavily in performance consistency during an Open. I would like to point out the sliver on Fig. 4, representing the number of athletes (six of them) finishing FOUR of five workouts within the top 60… and still being booted from the top 60 overall. This says, don’t screw up too badly on a single work out. And by too badly, the highest place for these six athletes: 304, 279, 395, 394, 511, and 449.
Maybe more to come… I started an ‘R’ script…
I cannot take credit for this. My husband wrote it on his blog domain (www.montegraphia.com), which is loading very slowly at the moment. His rationale for writing this manifests from my somewhat mediocre performance during the first week. I am a two-time individual Regionals and (team) Games competitor. I am committed to competing in the Southeast Regionals as an individual, but we should also have a strong team. That being said, I have to push fucking hard to make up for my inefficiencies with double-unders the first week, leaving me a best score of 308 (2nd attempt). Okay, enough about me.
PREDICTING REGIONAL COMPETITORS FROM SINGLE OPEN RESULTS
Let’s get this straight. The CrossFit Open is five workouts for a reason: a single result does not accurately predict whether you’ll be headed to the Regional competition, and Open results have even less predictive power for Games competitors. There is more to being the “Fittest on Earth” than performing well on a single, 10 minute, AMRAP.
Sure, these statements seem obvious. But I’m a scientist, and I like numbers. And, frankly, sometimes my wife is hysterical about her performance, to the point where she throws out words like ‘impossible’ after completing a single Open workout and not performing to her liking. So, I set out to look at some data, and challenge myself to calculate some probabilities (’cause this is how I show my affection…). I’m writing as I work (or waste time…), and I’m not confident that I can attain my goal, that is, to provide a probability distribution of qualifying for Regional competitions (~ top 50 Open competitors from each region), given a result from a single Open workout. I may just provide some graphs and a few numbers that suggest the first paragraph in this post is true, without actually getting to this distribution thing. After all, I’m an ecologist, am more or less self taught in statistics and probability, and I have a job. Plus, I like playing fetch with my dogs.
I’ve taken results from the 2012 and 2013 Open competitions from the top 180 finishing women in five US regions: South East, Central East, South West, Southern California, and Norther California. There’s much more data to be copied than what I’m working with, but I think these data are representative of the whole, and I couldn’t figure out how to access raw data without copy-paste.
From memory: In 2012, the top 60 went to Regionals, while in 2013 the top 48 were selected. Similarly, the top 48 will be selected in 2014. I’m rounding the selection to 50, given that there are probably a few qualifiers that will compete in a team or decline all together. This is likely a conservative selection cut-off.
A few plots (I’m not proud of these – they are quick and dirty Excel ‘charts’… don’t tell my students).
Fig. 1: Maximum workout placing across all five workouts for two years and five regions (women only).
Fig. 2: Minimum workout placing across all five workouts for two years and five regions (women only).
The first couple of plots are simple: of the top 180 women in five regions and across two years of the Open, what were their maximum and minimum placings? There is a lot of variation in both plots, and I was tempted to conclude that the scoring method of the Open, which is used to calculated the overall placing (x-axis), was weighted heavier for the maximum placing. I think I’d have to calculate a coefficient of variation to be sure though, given that the scales on the y-axis are pretty different for the two plots.
Bigger picture: There were no qualifying athletes (top 50) who placed lower than 268 in any one workout, and the average maximum placing was 90. Further, all qualifying athletes scored at least one workout below 60th place, with an average minimum score of about 15th. What this means to me is that if you want to qualify, don’t have any scores above 300, and score in the top 50 at least once (probably more… maybe that’ll be the next calculation: number of workouts placed in top 50 or 60). I round these numbers a bit for a couple reasons: (1) there are more competitors this year, and (2) the I suspect consistency (below top 60) is more important here.
The clock gene Bmal1 should ring a bell to my regular readers. Obviously, the qualifier “clock gene” indicates that Bmal1 is part of the molecular feedback loop that drives biological rhythms. Bmal1 also happens to be a priori in this molecular feedback loop because its transcription in the nucleus and translocation to the cytoplasm to undergo translation is one of the first (rate-limiting) steps of the molecular feedback loop. In the absence of Bmal1, the feedback loop, or biological clock for that matter, is broken, literally. There are very few, existing rhythms of behavior and physiology in mice lacking Bmal1. These mice also have poor health. They’re diabetic, hypersomniacs, arthritic, hypertensive, impotent, and die young. Basically, they represent a significant portion of the US population; er at least according to my primary care physician who always says that I will be the “healthiest person that I [he] will see in two months time.”
This weekend, I read a paper that addresses the mechanisms through which mice lacking Bmal1 have impaired metabolic cycles, namely for glycolysis and fatty acid oxidation. First off, mice lacking Bmal1 have fewer amounts of nicotinamide adenine dinucleotide (NAD+). If you recall the basic biochemical pathways of glycolysis, the Krebs cycle, and cellular respiration, you are well aware that NAD+ plays a pivotal role as the net amount of ATP (36-38 in total) available is dependent on the number of electrons carried by NAD. Thus, a limited availability of NAD+ means that less ATP is available to meet energy demands and more lactate–what is produced in the presence of anaerobic respiration and what is responsible for creating “a burn” and accelerating fatigue–would accumulate. This is exactly what happened. Bmal1 mutants have yet to be met with an exercise challenge, but I wager that they would perform very poorly.
Bmal1 mutants also have deficient oxygen uptake in the presence of substrates involved with cellular respiration–ADP, pyruvate, and fatty acids. In fact, oxygen uptake in the Bmal1 mutants is deficient to the point that there is little change in oxygen uptake in the presence of ADP versus the presence of biological agents that uncouple cellular respiration; by interfering with the shuttling of electrons along the electron transport chain or the production of ATP from ADP. What is most interesting is that these deficiencies are in the absence of a reduction in mitochondrial volume which would obviously affect the rate and extent of oxygen uptake.
There are many other deficits in biochemical pathways of the liver in Bmal1 mutants that were reported in this study, but I am most interested in those that relate to exercise. What would happen to these mice when challenged with high-intensity or endurance-based exercise? We already know that these mice don’t respond well to other environmental stressors like sleep loss so I would bet a few hundred dollars that they also fail miserably on tests of fitness.
Peek CB, Affinati AH, Ramsey KM, Kuo HY, Yu W, Sena LA, Ilkayeva O, Marcheva B, Kobayashi Y, Omura C, Levine DC, Bacsik DJ, Gius D, Newgard CB, Goetzman E, Chandel NS, Denu JM, Mrksich M, & Bass J (2013). Circadian clock NAD+ cycle drives mitochondrial oxidative metabolism in mice. Science (New York, N.Y.), 342 (6158) PMID: 24051248
This week, I participated in the second part of a cardiometabolic study in elite crossfitters. This time around, I actually did a 15 minute crossfit workout. The procedure was similar to last time in which they took measures of blood lactate after each completed round of exercises. It was enjoyable to see a rise in lactate levels corroborate with the point in the workout when I really began to fatigue and “feel the burn.” Yay, science! Apparently, I recover quickly which is the mental edge that I will need throughout the Open since the workouts take a toll on the mind and body.
Last week, a track and field record held for 20 years by Sergey Bubka using “unconventional” technique (that soon became conventional) was broken by the reigning Olympic medalist. Bubka was there to watch. Words cannot describe the years of work to achieve. So beautiful!