# Calorie claims of exercise classes and Strava

This machine burns more calories per minute than any other– up to 800 calories in a 45-minute session” (SWEAT by BXR | VersaClimbing)

Strava shows: Morning Elliptical 1h36m 1,115Cal 136bpm.

If the ad and Strava mean calories (small calories), not kilocalories (large calories), then the unit Cal must be a typo for cal – s

Both “up to 800 calories in a 45-minuteand 1,115 cal in 1h36m are less than the basal metabolic rate of 1200kcal/day =50kcal/h =50,000cal/h. The marketing claims probably intend to say kilocalories.

The sustained (an hour or more) power output of elite cyclists is about 400W. ()

1W =1J/s =3600J/h, so 400W =1440kJ/h =344 kcal/h because a calorie is 4.184 Joules.

Less-than-elite athletes likely burn significantly fewer calories than 344kcal in an hour, especially when not competing. Any exercise class, app or sports watch claiming over 200 kcal/h is suspect.

# Exaggerating vs hiding emotions

In some cultures, it was a matter of honour not to show emotions. Native American warriors famously had stony visages. Victorian aristocracy prided themselves in a stiff upper lip and unflappable manner. Winston Churchill describes in his memoirs how the boarding school culture, enforced by physical violence, was to show no fear. In other cultures, emotions are exaggerated. Teenagers in North America from 1990 to the present are usually portrayed as drama queens, as are arts people. Everything is either fabulous or horrible to them, no so-so experiences. I have witnessed the correctness of this portrayal in the case of teenagers. Jane Austen’s “Northanger Abbey” depicts Victorian teenagers as exaggerating their emotions similarly to their modern-day counterparts.

In the attention economy, exaggerating emotions is profitable to get and keep viewers. Traditional and social media portray situations as more extreme than these really are in order to attract eyeballs and clicks. Teenagers may have a similar motivation – to get noticed by their peers. Providing drama is an effective way. The notice of others may help attract sex partners or a circle of followers. People notice the strong emotions of others for evolutionary reasons, because radical action has a higher probability of following than after neutral communication. Radical action by others requires a quick accurate response to keep one’s health and wealth or take advantage of the radical actor.

A child with an injury or illness may pretend to suffer more than actually to get more care and resources from parents, especially compared to siblings. This is similar to the begging competition among bird chicks.

Exaggerating both praise and emotional punishment motivates others to do one’s bidding. Incentives are created by the difference in the consequences of different actions, so exaggerating this difference strengthens incentives, unless others see through the pretending. Teenagers may exaggerate their outward happiness and anger at what the parents do, in order to force the parents to comply with the teenager’s wishes.

On the other hand, in a zero-sum game, providing information to the other player cannot increase one’s own payoff and usually reduces it. Emotions are information about the preferences and plans of the one who shows these. In an antagonistic situation, such as negotiations or war between competing tribes, a poker face is an information security measure.

In short, creating drama is an emotional blackmail method targeting those with aligned interests. An emotionless front hides both weaknesses and strengths from those with opposed interests, so they cannot target the weakness or prepare for the precise strength.

Whether teenagers display or hide emotion is thus informative about whether they believe the surrounding people to be friends or enemies. A testable prediction is that bullied children suppress emotion and pretend not to care about anything, especially compared to a brain scan showing they actually care and especially when they are primed to recall the bullies. Another testable prediction is that popular or spoiled children exaggerate their emotions, especially around familiar people and when they believe a reward or punishment is imminent.

# P-value cannot be less than 1/1024 in ten binary choices

Baez-Mendoza et al (2021) claim that for rhesus macaques choosing which of two others to reward in each trial, „the difference in the other’s reputation based on past interactions (i.e., how likely they were to reciprocate over the past 20 trials) had a significant effect on the animal’s choices [odds ratio (OR) = 1.54, t = 9.2, P = 3.5 × 10^-20; fig. S2C]”.

In 20 trials, there are ten chances to reciprocate if I understand the meaning of reciprocation in the study (monkey x gives a reward to the monkey who gave x a reward in the last trial). Depending on interpretation, there are 6-10 chances to react to reciprocation. Six if three trials are required for each reaction: the trial in which a monkey acts, the trial in which another monkey reciprocates and the trial in which a monkey reacts to the reciprocation. Ten if the reaction can coincide with the initial act of the next action-reciprocation pair.

Under the null hypothesis that the monkey allocates rewards randomly, the probability of giving the reward to the monkey who previously reciprocated the most 10 times out of 10 is 1/1024. The p-value is the probability that the observed effect is due to chance, given the null hypothesis. So the p-value cannot be smaller than about 0.001 for a 20-trial session, which offers at most 10 chances to react to reciprocation. The p-value cannot be 3.5*10^-20 as Baez-Mendoza et al (2021) claim. Their supplementary material does not offer an explanation of how this p-value was calculated.

Interpreting reciprocation or trials differently so that 20 trials offer 20 chances to reciprocate, the minimal p-value is 1/1048576, approximately 10^-6, again far from 3.5*10^-20.

A possible explanation is the sentence “The group performed an average of 105 ± 8.7 (mean ± SEM) trials per session for a total of 22 sessions.” If the monkey has a chance to react to past reciprocation in a third of the 105*22 sessions, then the p-value can indeed be of the order 10^-20. It would be interesting to know how the authors divide the trials into the reputation-building and reaction blocks.

# Animal experiments on whether pose and expression control mood

Amy Cuddy promoted power poses which she claimed boosted confidence and success. Replication of her results failed (the effects were not found in other psychology studies), then succeeded again, so the debate continues. Similarly, adopting a smiling expression makes people happier. Measuring the psychological effects of posture and expression is complicated in humans. For example, due to experimenter demand effects. Animals are simpler and cheaper to experiment with, but I did not find any animal experiments on power poses on Google Scholar on 28.03.2021.

The idea of the experiment is to move the animal into a confident or scared pose and measure the resulting behaviour, stress hormones, dominance hormones, maybe scan the brain. Potentially mood-affecting poses differ between animals, but are well-known for common pets. Lifting a dog’s tail up its back is a confident pose. Moving the tail side to side or putting the chest close to the ground and butt up in a “play-with-me bow” is happy, excited. Putting the dog’s tail between the legs is scared. Moving the dog’s gums back to bare its teeth is angry. Arching a cat’s back is angry. Curling the cat up and half-closing its eyes is contented.

The main problem is that the animal may resist being moved into these poses or get stressed by the unfamiliar treatment. A period of habituation training is needed, but if the pose has an effect, then part of this effect realises during the habituation. In this case, the measured effect size is attenuated, i.e. the pre- and post-treatment mood and behaviour look similar.

A similar experiment in people is to have a person or a robot move the limbs of the participants of the experiment into power poses instead of asking them to assume the pose. The excuse or distraction from the true purpose of the experiment may be light physical exercise, physical therapy or massage. This includes a facial massage, which may stretch the face into a smile or compress into a frown. The usual questionnaires and measurements may be administered after moving the body or face into these poses or expressions.

# Improving the accuracy of waist circumference measurement

Waistline needs to be measured for clothes size determination and health evaluation, but the instructions to do it are vague: keep the stomach neutral, neither sucked in nor bulged out, hold the measuring tape not too loosely nor too tight. A person’s waist expands and contracts with the breath, so at which point of the breath cycle should the measurement be taken? How much tension should the measuring tape have?

Averaging several measurements would be more accurate than a single vaguely defined one, both in the sense of replication across different measurers and in the sense of corresponding to the physical quantity of interest (health status, tightness of the clothes). One simple way is to average the measured perimeter of a maximally sucked-in gut and a maximally pushed-out gut.

A better measure, but quite difficult to take, is to average the waist circumference across the whole cycle of breathing in and out, for several cycles. A device to do this would be a loop of flexible measuring tape pulled into a roll by a spring, similarly to a construction measuring tape made of metal. The roll would contain electronics which continuously record how much tape is in the roll, therefore the length of the loop outside the roll. The length data could be averaged to obtain the average waistline.

A more modern method is to use two cameras pointed at the person from two sides at waist height, recording a 3D video of the waist over the breath cycle. The video could then be averaged on a computer to find the mean volume. Whole-body volume could be determined similarly – no need for Archimedes’ Eureka method of submerging the body in a bathtub with a volume scale on it. The volume and the weight determine the density of the body, which gives partial information about its fat percentage.

# Moon phase and sleep correlation is not quite a sine wave

Casiraghi et al. (2021) in Science Advances (DOI: 10.1126/sciadv.abe0465) show that human sleep duration and onset depends on the phase of the moon. Their interpretation is that light availability during the night caused humans to adapt their sleep over evolutionary time. Casiraghi et al. fit a sine curve to both sleep duration and onset as functions of the day in the monthly lunar cycle, but their Figure 1 A, B for the full sample and the blue and orange curves for the rural groups in Figure 1 C, D show a statistically significant deviation from a sine function. Instead of same-sized symmetric peaks and troughs, sleep duration has two peaks with a small trough between, then a large sharp trough which falls more steeply than rises, then two peaks again. Sleep onset has a vertically reflected version of this pattern. These features are statistically significant, based on the confidence bands Casiraghi and coauthors have drawn in Figure 1.

The significant departure of sleep patterns from a sine wave calls into question the interpretation that light availability over evolutionary time caused these patterns. What fits the interpretation of Casiraghi et al. is that sleep duration is shortest right before full moon, but what does not fit is that the duration is longest right after full and new moons, but shorter during a waning crescent moon between these.

It would better summarise the data to use the first four terms of a Fourier series instead of just the first term. There seems little danger of overfitting, given N=69 and t>60.

A questionable choice of the authors is to plot the sleep duration and onset of only the 35 best-fitting participants in Figure 2. A more honest choice yielding the same number of plots would pick every other participant in the ranking from the best fit to the worst.

In the section Materials and Methods, Casiraghi et al. fitted both a 15-day and a 30-day cycle to test for the effect of the Moon’s gravitational pull on sleep. The 15-day component was weaker in urban communities than rural, but any effect of gravity should be the same in both. By contrast, the effect of moonlight should be weaker in urban communities, but the urban community data (Figure 1 C, D green curve) fits a simple sine curve better than rural. It seems strange that sleep in urban communities would correlate more strongly with the amount of moonlight, like Figure 1 shows.

# Clinical trials of other drugs in other species to predict a drug’s effect in humans

Suppose we want to know whether a drug is safe or effective for humans, but do not have data on what it does in humans, only on its effects in mice, rats, rhesus macaques and chimpanzees. In general, we can predict the effect of the drug on humans better with the animal data than without it. Information on “nearby” realisations of a random variable (effect of the drug) helps predict the realisation we are interested in. The method should weight nearby observations more than observations further away when predicting. For example, if the drug has a positive effect in animals, then predicts a positive effect in humans, and the larger the effect in animals, the greater the predicted effect in humans.

A limitation of weighting is that it does not take into account the slope of the effect when moving from further observations to nearer. For example, a very large effect of the drug in mice and rats but a small effect in macaques and chimpanzees predicts the same effect in humans as a small effect in rodents and a large one in monkeys and apes, if the weighted average effect across animals is the same in both cases. However, intuitively, the first case should have a smaller predicted effect in humans than the second, because moving to animals more similar to humans, the effect becomes smaller in the first case but larger in the second. The idea is similar to a proportional integral-derivative (PID) controller in engineering.

The slope of the effect of the drug is extra information that increases the predictive power of the method if the assumption that the similarity of effects decreases in genetic distance holds. Of course, if this assumption fails in the data, then imposing it may result in bias.

Assumptions may be imposed on the method using constrained estimation. One constraint is the monotonicity of the effect in some measure of distance between observations. The method may allow for varying weights by adding interaction terms (e.g., the effect of the drug times genetic similarity). The interaction terms unfortunately require more data to estimate.

Extraneous information about the slope of the effect helps justify the constraints and reduces the need for adding interaction terms, thus decreases the data requirement. An example of such extra information is whether the effects of other drugs that have been tested in these animals as well as humans were monotone in genetic distance. Using information about these other drugs imposes the assumption that the slopes of the effects of different drugs are similar. The similarity of the slopes should intuitively depend on the chemical similarity of the drugs, with more distant drugs having more different profiles of effects across animals.

The similarity of species in terms of the effects drugs have on them need not correspond to genetic similarity or the closeness of any other observable characteristic of these organisms, although often these similarities are similar. The similarity of interest is how similar the effects of the drug are across these species. Estimating this similarity based on the similarity of other drugs across these animals may also be done by a weighted regression, perhaps with constraints or added interaction terms. More power for the estimation may be obtained from simultaneous estimation of the drug-effect-similarity of the species and the effect of the drug in humans. An analogy is demand and supply estimation in industrial organisation where observations about each side of the market give information about the other side. Another analogy is duality in mathematics, in this case between the drug-effect-similarity of the species and the given drug’s similarity of effects across these species.

The similarity of drugs in terms of their effects on each species need not correspond to chemical similarity, although it often does. The similarity of interest for the drugs is how similar their effects are in humans, and also in other species.

The inputs into the joint estimation of drug similarity, species similarity and the effect of the given drug in humans are the genetic similarity of the species, the chemical similarity of the drugs and the effects for all drug-species pairs that have been tested. In the matrix where the rows are the drugs and the columns the species, we are interested in filling in the cell in the row “drug of interest” and the column “human”. The values in all the other cells are informative about this cell. In other words, there is a benefit from filling in these other cells of the matrix.

Given the duality of drugs and species in the drug effect matrix, there is information to be gained from running clinical trials of chemically similar human-use-approved drugs in species in which the drug of interest has been tested but the chemically similar ones have not. The information is directly about the drug-effect-similarity of these species to humans, which indirectly helps predict the effect of the drug of interest in humans from the effects of it in other species. In summary, testing other drugs in other species is informative about what a given drug does in humans. Adapting methods from supply and demand estimation, or otherwise combining all the data in a principled theoretical framework, may increase the information gain from these other clinical trials.

Extending the reasoning, each (species, drug) pair has some unknown similarity to the (human, drug of interest) pair. A weighted method to predict the effect in the (human, drug of interest) pair may gain power from constraints that the similarity of different (species, drug) pairs increases in the genetic closeness of the species and the chemical closeness of the drugs.

Define Y_{sd} as the effect of drug d in species s. Define X_{si} as the observable characteristic (gene) i of species s. Define X_{dj} as the observable characteristic (chemical property) j of drug d. The simplest method is to regress Y_{sd} on all the X_{si} and X_{dj} and use the coefficients to predict the Y_{sd} of the (human, drug of interest) pair. If there are many characteristics i and j and few observations Y_{sd}, then variable selection or regularisation is needed. Constraints may be imposed, like X_{si}=X_i for all s and X_{dj}=X_j for all d.

Fused LASSO (least absolute shrinkage and selection operator), clustered LASSO and prior LASSO seem related to the above method.

# Virulence of a disease may cause vaccines to be effective

My uninformed speculation: vaccines may be so effective against Covid-19 (90-95% vs flu vaccine 70%) for the same reason why Covid-19 is so infectious – it binds strongly to biochemicals in the organism. If high affinity to the angiotensin-converting enzyme 2 on the surfaces of lung cells is positively correlated with strong binding to antibodies and immune cells, then the immune system, once triggered, removes the viral particles faster for those respiratory viruses that infect cells more easily. Strong binding and the consequent intense immune triggering may also be the reason for the life-threatening immune overreaction (cytokine storm) to the novel coronavirus.
This hypothesis could be tested on a cross-sectional dataset of viral diseases using some measure of the infectiousness of a disease, the effectiveness of a vaccine against it and the frequency of immune overreaction to it.
Infectiousness may be measured by ID50: what number of microbes makes half the organisms exposed to this number sick. This measure depends on the state of the organisms studied. For example, if people’s immune system is weaker in the winter on average, then ID50 measured in the winter is lower than in the summer.
Vaccine effectiveness is typically measured in percent – what fraction of vaccinated people are protected, in the sense that they do not catch the disease in circumstances in which unvaccinated people catch it. This measure of may depend on what the exposure to the disease is. For example, if a large enough dose of the microbe makes everyone sick, vaccinated or no, then exposure to this dose shows zero effect of the vaccine. Similarly, if a small enough dose fails to infect anyone, then the vaccine effect seems zero, but at least the lack of infections among the unvaccinated shows that no information about vaccine efficacy can be obtained from this exposure test.
Immune overreaction needs to be confidently ascribable to the disease studied for it to be a relevant measure for testing the theory about the connection between virulence and vaccine efficacy.

# Tissue sampling by piggybacking on vaccination or testing campaigns

Obtaining tissue samples from a large population of healthy individuals is useful for many research and testing applications. Establishing the distribution of genes, transcriptomes, cell distributions and morpologies in a normal population allows comparing clinical laboratory findings to reference values obtained from this baseline. The genetic composition of the population can be used to estimate historical migration patterns in paleoanthropology and selective pressures in evolutionary biology.

Gathering tissue samples from many people is expensive and time-consuming, unless it happens as a byproduct of existing programs. Collecting used vaccination needles or coronavirus nasal swabs that have a few cells attached allows anonymous tissue sampling of almost the entire population. A few cells per person are enough for many analyses in modern biology. Bulk collection of needles or swabs has built-in untraceability of biological material to an individual, which should alleviate privacy concerns and reduce the bureaucratic burden of ethics approvals.

# Algae tattooed for doping and oxygen administration

The paper by Qiao et al. (2020) in Science Advances shows that unicellular algae injected near a hypoxic tumour photosynthesise oxygen in the body in response to infrared light with wavelength 660nm that penetrates >4mm into tissues. The oxygen saturation of the tumour rises from 6.2% to 30% in 2 hours after the algae receive a 5-minute laser exposure. The oxygen sensitises the tumour to radiation therapy. No side effects were found from the algae in this or previous research. The performance of the algae stayed the same when these were coated with red blood cell membranes to delay their clearance from the body.

Another application of algae that can produce oxygen in the organism is doping in sports. The algae can be tattooed under skin that is exposed to light containing enough of the wavelengths which the algae use and which can penetrate under the skin. For example, long-distance runners outdoors in warm weather have most of their skin exposed to sunlight, thus have a large surface area suitable for algal oxygen production. The additional oxygen from photosynthesis improves athletic performance. The only question is whether the oxygen generation is quantitatively fast enough to make a difference. In elite sports, every little advantage counts, so athletes are probably willing to use algae tattoos.

The algae are not dangerous even in deep blood vessels and tissues. Eventually the organism clears the algae, but the clearance of foreign particles is slower in the skin than in deep tissues, as evidenced by the persistence of ordinary tattoos. So the algae will last for a daylong competition.

Patients with breathing problems, for example with coronavirus-induced lung inflammation, may also benefit from algae tattooed on a large area of skin which is then illuminated with 660nm light. Such oxygen supplementation reduces the need for mechanical ventilation. Again, the question is the amount of oxygen from a whole-body algal tattoo.