Since my retirement, I’ve started gardening with native plants. Originally this was an interest of my wife’s, but through her I became interested too. We live in a traditional suburban neighborhood, with most of the homes having primarily turf grass lawns that are maintained with a lot of water, fertilizer, and herbicides. But whether the neighbors like it or not, we have changed. Each year, we convert more and more of our yard to native flower gardens. We have a rain garden in a low spot in the back yard, and several other gardens are back there too. In the front, under our crabapple and serviceberry trees, we have all sorts of flowers, including goldenrods and asters.
The point of native gardening is not just to have pretty flowers. Our main goal is to support the native birds, butterflies, bees, and other animals. Evolution creates complex and interdependent ecosystems, where the flowers rely on the bees and butterflies for pollination, the bees and butterflies need the pollen and nectar for food, and the birds eat the caterpillars (soon to be butterflies) and flower seeds. We face a biodiversity crisis in our society that we can address, in a small way, with native gardening. One book that influenced me in this endeavor is Doug Tallamy’s Nature’s Best Hope: A New Approach to Conservation that Starts in Your Yard. I highly recommend it.
I’m still a physicist, interested in the applications of physics to medicine and biology. There’s lots of physics in native gardening, which I intend to explore. So, over the next few weeks I’ll post a series of essays about the physics of native gardening. Next week will be the physics of birds, the following week the physics of bees, and the third week the physics of butterflies. Some topics will be drawn from Intermediate Physics for Medicine and Biology, but most will come from other sources.
You might ask, why just birds, bees, and butterflies? Why not bats, beavers, and beetles? Fair question. We have many other animals visiting our yard. We’ve been trying to attract frogs and toads, but without a pond or stream it is difficult. Worms are a crucial asset for any gardener, especially if you maintain a compost heap like we do. Other insects we often see include the desirable dragonflies, moths, and lightning bugs, and some undesirable ones like wasps, flies, and mosquitoes. We have small mammals such as squirrels (who do their best to invade our bird feeders, but that’s another story), rabbits, and chipmunks. Our yard is too small to support larger predators, like foxes or coyotes, but we do occasionally have a Cooper’s hawk visit. On rare occasion, we see a raccoon, skunk, possum, or ground hog, but I don’t think they live here. One large mammal that comes a lot is white-tailed deer, which wander in from a nearby forest. They only eat plants, and act like giant rabbits as far as their ecological niche. I would enjoy having a big oak tree, but we don’t. We do have a couple maples, and a linden tree growing up in the middle of our deck. We love all these plant and animals.
It’s not my habit to do a series of postings on related topics. But biodiversity is important. Moreover, it’s cold and snowy here in Michigan right now so I can’t go out and dig, weed, water, or plant. I’ll do the next best thing and write. I hope you enjoy it.
Tune in next week as we explore the physics of birds.
Want a sneak peek at one of the new homework problems tentatively included in the 6th edition of Intermediate Physics for Medicine and Biology? Today I present a problem related to the flawed “cyclotron resonance hypothesis.” A lot of nonsense has been written about the idea of extremely low frequency electromagnetic fields influencing biology and medicine, and one of the proposed mechanisms for such effects is cyclotron resonance.
One important application of magnetic forces in medicine
is the cyclotron. Many hospitals have a cyclotron for the
production of radiopharmaceuticals, especially for generating
positron-emitting nuclei for use in Positron EmissionTomography (PET) imaging (see Chap. 17).
Consider a particle of charge q and mass m, moving with
speed v in a direction perpendicular to a magnetic field B.
The magnetic force will bend the path of the particle into a
circle. Newton’s second law states that the mass times the
centripetal acceleration, v2/r, is equal to the magnetic force
mv2/r = qvB. (8.5)
The speed is equal to [the] circumference of the circle, 2πr,
divided by the period of the orbit, T. Substituting this
expression for v into Eq. (8.5) and simplifying, we find
T = 2π m/(qB). (8.6)
In a cyclotron particles orbit at the cyclotron frequency,
f = 1/T. Because the magnetic force is perpendicular to the
motion, it does not increase the particles’ speed or energy. To
do that, the particles are subjected periodically to an electric
field that changes direction with the cyclotron frequency so
that it is always accelerating, not decelerating, the particles.
This would be difficult if not for the fortuitous disappearance
of both v and r from Eq. (8.6), so that the cyclotron frequency
only depends on the charge-to-mass ratio of the particles and
the magnetic field, but not on their energy.
This analysis of cyclotron motion works great in a vacuum. The trouble begins when you apply the cyclotron concept to ions in the conducting fluids of the body. The proposed hypothesis says that when an ion is moving about in the presence of the earth’s magnetic field, the resulting magnetic force causes it to orbit about the magnetic field lines, with an orbital period equal to the reciprocal of the cyclotron frequency. If any electric field is present at that same frequency, it could interact with the ion, increasing its energy or causing it to cross the cell membrane.
Below is a draft of the new homework problem, which I hope debunks this erroneous hypothesis.
Section 9.1
Problem 7. One mechanism for how organisms are influenced by extremely low frequency electric fields is the cyclotron resonance hypothesis.
(a) The strength of the earth's magnetic field is about 5 × 10–5T. A calcium ion has a mass of 6.7 × 10–26kg and a charge of 3.2 × 10–19C. Calculate the cyclotron frequency of the calcium ion. If an electric field exists in the tissue at that frequency, the calcium ion will be in resonance with the cyclotron frequency, which could magnify any biological effect.
(b) This mechanism seems to provide a way for an extremely low frequency electric field to interact with calcium ions, and calcium influences many cellular processes. But consider this hypothesis in more detail. Use Eq. 4.12 to calculate the root-mean-square speed of a calcium ion at body temperature. Use this speed in Eq. 8.5 to calculate the radius of the orbit. Compare this to the size of a typical cell.
(c) Now make a similar analysis, but assume the radius of the calcium ion orbit is about the size of a cell (since it would have difficulty crossing the cell membrane). Then use this radius in Eq. 8.5 to determine the speed of the calcium ion. If this is the root-mean-square speed, what is the body temperature?
(d) Finally, compare the period of the orbit to the time between collisions of the calcium ion with a water molecule. What does this imply for the orbit?
This analysis should convince you that the cyclotron resonance hypothesis is unlikely to be correct. Although the frequency is reasonable, the orbital radius will be huge unless
the ions are traveling extraordinarily slowly. Collisions with water molecules will completely disrupt the orbit.
For those who don't have the 5th edition of IPMB handy, Eq. 4.12 says the root-mean-square speed is equal to the square root of 3 times Boltzmann's constant times the absolute temperature divided by the mass of the particle.
I won’t give away the solution to this problem (once the 6th edition of IPMB is out, instructors can get the solution manual for free by emailing me at roth@oakland.edu). But here are some order-of-magnitude results. The cyclotron frequency is tens of hertz. The root-mean-square (thermal) speed of calcium at body temperature is hundreds of meters per second. The resulting orbital radius is about a meter. That is bigger than the body, and vastly bigger than a cell. To fit the orbit inside a cell, the speed would have to be much slower, on the order of a thousandth of a meter per second, which corresponds to a temperature of about a few nanokelvins. The orbital period is a couple hundredths of a second, but the time between collisions of the ion with a water molecule is one the order of 10–13 seconds, so there are many billions of collisions per orbit. Any circular motion will be destroyed by collisions long before anything like an orbit is established. I’m sorry, but the hypothesis is rubbish.
Finally, for you folks who are really on the ball, you may be wondering why this homework problem is listed as being in Chapter 9 when the discussion of the cyclotron is in Chapter 8 of the 5th edition of IPMB. (In this post I changed the equation numbers in the homework problem to match the 5th edition, so you would have them.) Hmm.. is there a new chapter in the 6th edition? More on that later…
To be fair, I should let my late friend Abraham Liboff tell you his side of the story. In this video, Abe explains how he proposed the cyclotron resonance hypothesis. I liked Abe, but I didn’t like his hypothesis.
Today’s question is: do mutations happen randomly, or are they caused by some selective pressure? In other words, are mutations a Darwinian event where they happen by chance and then natural selection selects those that are favorable to pass on to the offspring, or are mutations Lamarckian where they happen because they help a species survive (like a giraffe constantly stretching its neck to reach the leaves at the top of the tree, thereby making its neck longer, and then passing that acquired trait to its offspring). To determine which of these two hypotheses is correct, we need an experimental test.
Let’s examine one famous experiment. To make things simple, consider a specific case. Assume we start with just one individual, who is not a mutant. Furthermore, let each parent have two offspring, and only analyze three generations. For the first two generations there is no selective pressure, and only in the third generation the selective pressure is present. To make the analysis really simple, assume the probability of a mutation, p, is very small.
The most common case is shown in the figure below. Blue circles represent the individuals in each generation, starting in the first generation with just one. Locations where lines branch represent births. (Wait, you say, each child should have two parents, not one! Okay, we are making a simple model. Assume an individual reproduces asexually by splitting into two. We should talk about “splittings” and not “births.”) The green dashed line represents when the selective pressure begins. So our picture shows one great-grandparent, two grandparents, four parents, and eight children. A mutation is indicated by changing a blue circle to red.
Because p << 1, by far the most common result is shown below, with no mutations.
Lamarckian Evolution
In the case when mutations are caused by some selective pressure (Lamarckian), you can get a more interesting situation like shown below. No one above the dashed line undergoes a mutation because there was no selective pressure then. A child below the dashed line in the bottom row might have a mutation. There are eight children, so the probability of one of the eight having a mutation is 8p. The probability of two offspring having mutations will go as p2, but since we are assuming p is small the odds of having multiple mutant offspring will be negligible. We’ll ignore those cases.
Let’s calculate some statistics for this case. Let n be the number of mutant offspring in the last generation (below the dashed line). To find the average value, or mean, of n over several experiments, which we’ll call <n>, you sum up all the possible cases, each multiplied by its probability. In general, we could have n = 0, 1, 2, …, 8, each with probability p0, p1, …, p8, so <n> is
<n> = p0 (0) + p1 (1) + p2 (2) + … + p8 (8).
But in this case p2, p3, …, p8 are all negligibly small, so we have only the first two terms in the sum to worry about.
For each individual, the odds of not mutating is (1 – p). In the last generation below the dashed line there are 8 offspring, so the probability of none of them having a mutation, p0, is (1 – 8p). The probability for one mutation (p1) is 8p because there are 8 offspring, each with probability p of mutating. So
<n> = (1 – 8p) (0) + 8p (1) = 8p .
We will also be interested in the variation of results between different trials. For this, we need <n2>
But remember, p << 1 so the last term is negligible and the variance is 8p. Therefore, the mean and variance are the same. You may have seen a probability distribution with this property before. Appendix J of IPMB states that the Poisson distribution has the same mean and variance. Basically, the Lamarckian case is a Poisson process.
Darwinian Evolution
Now consider the case when mutations occur randomly (Darwinian). You still can get all the results shown earlier in the Lamarckian case, but you get others too because mutations can happen all the time, not just when the selective pressure is operating. Suppose one of the parents (just above the dashed line) mutates. Their mutation gets passed to both offspring. The odds of mutating back (changing from red to blue) are very small (p << 1), so we assume both offspring of a mutant inherit the mutation, as shown below.
You could also have one of the two grandparents give rise to four mutant offspring below the dashed line, as shown below.
Let’s do our statistics again. As before, the vast majority of the cases have no mutations. There are now 14 cases, each of which could have the mutation in one of the offspring. All the cases are shown below.
The probability of having no mutations ever (the bottom right case) is (1 – 14p). The probability of one of the offspring having a mutation is 8p (the eight cases in the top row). The probability of any one of the parents having a mutation is p and there are 4 parents, so the probability of a mutation among the parents is 4p, and each would give rise to two mutants below the dashed line (the four cases on the left in the bottom row). Finally, one of the two grandparents could mutate (the fifth and sixth cases in the bottom row), each with probability p. If a grandparent mutates it results in 4 mutants below the dashed line. So, the mean number of mutants in the final generation is
The odds of a mutant appearing in the final generation is three times higher in the Darwinian case than in the Lamarckian case. What about the variance?
(remember, terms in p2 are negligible). Now the variance (56p) is over twice the mean (24p). It is not a Poisson process. It’s something else. There is much more variation in the number of mutants because of mutations happening early in the family tree that pass the mutation to all of the subsequent offspring.
Conclusion
In an experiment, p may not be easy to determine. You need to know how many individuals you start with (in our example, one) and how many generations you examine (in our example, three), as well as how many mutants you end up with. But you can easily compare the variance to the mean; just take their ratio (variance/mean). If they are the same, you suspect a Lamarckian Poisson process. If the variance is significantly more than the mean, you suspect Darwinian selection. In our example, variance/mean = 2.3.
There are some limitations. The probability is not always very small, so you might need to extend this analysis to cases where you have more than one mutation occurring. Also, in many experiments you will want to let the number of generations be much larger than three. There is also the possibility of a mutant mutating back to its original state. Finally, during sexual reproduction you have the in-laws to worry about, and you could have more than two offspring. So, to be quantitative you have some more work to do. But even in the more general case, the qualitative conclusion remains the same: Darwinian evolution results in a larger variance in the number of mutants than does Lamarckian evolution.
I suspect you now are saying “this is an interesting result; has anyone done this experiment?” The answer is yes! Salvador Luria and Max Delbrück did the experiment using E. coli bacteria (so the asexual splitting of generations is appropriate). The selective pressure applied at the end was resistance to a bacteriophage (a virus that infects bacteria). Their result: there was a lot more variation than you would expect from a Poisson process. Evolution is Darwinian, not Lamarckian. Mutations happen all the time, regardless of if there is some evolutionary pressure present.
What is electromagnetic hypersensitivity? It’s an alleged condition in which a person is especially sensitive to weak radiofrequency electromagnetic fields, such as those emitted by a cell phone or other wireless technology. All sorts of symptoms are claimed to be associated with electromagnetic hypersensitivity, such as headaches, fatigue, anxiety, and sleep disturbances. An example of a person who says he has electromagnetic hypersensitivity is Arthur Firstenberg, author of The Invisible Rainbow, a book about his trials and tribulations. Many people purportedly suffering from electromagnetic hypersensitivity flock to Green Bank, West Virginia, because a radiotelescope there requires that the surrounding area being a “radio quiet zone.”
Is electromagnetic hypersensitivity real? Answering this question should be easy. Take people who claim such hypersensitivity, sit them down in a lab, turn a radiofrequency device on (or just pretend to), and ask them if they can sense it. Ask them about their symptoms. Of course, you must do this carefully, avoiding any subtle cues that might signal if the radiation is present. (For a cautionary tale about why such care is important, read this post.) You should do the study double blind (neither the patient nor the doctor who asks the questions should be told if the radiation is or is not on) and compare the patients to control subjects.
The
effects of radiofrequency electromagnetic fields exposure on human
self-reported symptoms.
Many such experiments have been done, and recently a systematic review of the results was published.
This review is part of an ongoing project by the World Health Organization to assess potential health effects from exposure to radiofrequency electromagnetic fields. The authors come from a variety of countries, but several work at the respected Swiss Tropical and Public Health Institute.
I’m particularly familiar with the fine research of Martin Röösli, a renowned leader in this field.
The authors surveyed all publications on this topic and established stringent eligibility criteria so only the highest quality papers were included in their review. A total of 41 studies met the criteria. What did they find? Here’s the key conclusion from the author’s abstract.
The available evidence suggested that study volunteers could not perceive the EMF [electromagnetic field] exposure status better than what is expected by chance and that IEI-EMF [Idiopathic environmental intolerance attributed to electromagnetic fields, their fancy name for electromagnetic hypersensitivity] individuals could not determine EMF conditions better than the general population.
The patients couldn’t determine if the fields were on or off better than chance. In other words, they were right about the field being on or off about as often as if they had decided the question by flipping a coin. The authors added
Available evidence suggests that [an] acute RF-EMF [radiofrequency electromagnetic field] below regulatory limits does not cause symptoms and corresponding claims in... everyday life are related to perceived and not to real EMF exposure status.
Let me repeat, the claims are related “to perceived and not to real EMF exposure.” This means that electromagnetic hypersensitivity is not caused by an electromagnetic field being present, but is caused by thinking that an electromagnetic field is present.
Yes, there are some limitations to this study, which are discussed and analyzed by the authors. The experimental conditions might differ from real-life exposures in the duration, frequency, and location of the field source. Most of the subjects were young, healthy volunteers, so the authors could not make conclusions about the elderly or chronically ill. The authors could not rule out the possibility that a few super-sensitive people are mixed in with a vast majority who can’t sense the fields (although they do offer some evidence suggesting that this is not the case).
Their results do not prove that a condition like electromagnetic
hypersensitivity is impossible. Impossibility proofs are always
difficult in science, and especially in medicine and biology. But the
evidence suggests that the patients’ symptoms are related “to perceived
and not to real EMF exposure.” While I don’t doubt that these patients are
suffering, I’m skeptical that their distress is caused by
electromagnetic fields.
J. Patrick Reilly died on October 28 in Silver Spring, Maryland, at the age of 87. He was a leader in the field of bioelectricity, and especially the study of electrical stimulation.
Reilly was also known for his 1998 book Applied Bioelectricity: From Electrical Stimulation to Electropathology, which covered many of the same topics as Chapters 6–8 in IPMB: The Hodgkin-Huxley model of a nerve action potential, the electrical properties of cardiac tissue, the strength-duration curve, the electrocardiogram, and magnetic stimulation. However, you can tell that Russ and I are physicists while Reilly is an engineer. Applied Bioelectricity focuses less on deriving equations from fundamental principles and providing insights using toy models, and more on predicting stimulus thresholds, analyzing stimulus wave forms, examining electrode types, and assessing electrical injuries. That’s probably why he included the word “Applied” in his title. Compared to IPMB, Applied Bioelectricity has no homework problems, fewer equations, a similar number of figures, more references, and way more tables.
Reilly’s preface begins
The use of electrical devices is pervasive in modern society. The same electrical forces that run our air conditioners, lighting, communications, computers, and myriad other devices are also capable of interacting with biological systems, including the human body. The biological effects of electrical forces can be beneficial, as with medical diagnostic devices or biomedical implants, or can be detrimental, as with chance exposures that we typically call electric shock. Whether our interest is in intended or accidental exposure, it is important to understand the range of potential biological reactions to electrical stimulation.
I don’t recall meeting Reilly, which is a bit surprising given the overlap in our research areas. I certainly have been aware of his work for a long time. He was a skilled musician as well as an engineer. I would like to get a hold of his book Snake Music: A Detroit Memoir. It sounds like he had a difficult childhood, and there were many obstacles he had to overcome to make himself into a leading expert in bioelectricity. Thank goodness he persevered. J. Patrick Reilly, we’ll miss ya.
Figure 16.25 shows the evolution of the detector and
source configurations [of CT]. The third generation configuration is
the most popular. All of the electrical connections are made
through slip rings. This allows continuous rotation of the
gantry and scanning in a spiral as the patient moves through
the machine. Interpolation in the direction of the axis of rotation
(the z axis) is used to perform the reconstruction for a
particular value of z. This is called spiral CT or helical CT.
Kalender (2011) discusses the physical performance of CT
machines, particularly the various forms of spiral machines.
Kalender obtained his PhD in 1979 from the University of Wisconsin’s famous medical physics program. He then went to the University of Tübingen in Germany. There, according to Wikipedia, “he took and successfully completed all courses in the pre-clinical medicine curriculum.” This is interesting, because just a few years earlier Russ Hobbie did the same thing in Minnesota.
Between 1971 and 1973 I audited all the courses medical students take in their first 2 years at the University of Minnesota. I was amazed at the amount of physics I found in these courses and how little of it is discussed in the general physics course.
With deep sadness, the ESR announces the passing of Prof. Willi Kalender on October 20, 2024 at the age of 75. A pioneering figure in diagnostic imaging and medical physics, Prof. Kalender significantly influenced the field through his groundbreaking research and leadership.
Diffusion MRI was born in the mid-1980s. Since then, it
has enjoyed incredible success over the past 40 years, both
for research and in the clinical field. Clinical applications
began in the brain, notably in the management of acute
stroke patients. Diffusion MRI then became the standard
for the study of cerebralwhite-matter diseases, through the
diffusion tensor imaging (DTI) framework, revealing abnormalities
in the integrity of white-matter fibers in neurologic
disorders and, more recently, mental disorders. Over time,
clinical applications of diffusion MRI have been extended,
notably in oncology, to diagnose and monitor cancerous
lesions in almost all organs of the body. Diffusion MRI
has become a reference-imaging modality for prostate and
breast cancer. Diffusion MRI began in my hands in
1984 (I was then a radiology resident and a PhD student in
nuclear and particle physics) with my intuition that measuring
the molecular diffusion of water would perhaps allow to
characterize solid tumors due to the restriction of molecular
motion and vascular lesions where in circulating blood
“diffusion” would be somewhat enhanced. This idea was
to become the cornerstone of diffusion MRI. This article
retraces the early days and milestones of diffusion MRI
which spawned over 40 years.
I knew Le Bihan when I worked at the intramural program of the National Institutes of Health in the late 1980s and early 1990s. To me, he was mainly Peter Basser’s French friend. Peter was my colleague who worked in the same section as I did (his office was the second office down the hall from mine), and was my best friend at NIH. Le Bihan describes the start of his collaboration with Basser this way:
During the “NIH Research Festival” of October 1990 I
met Peter Basser who had a poster on ionic fluxes in tissues
while I had a talk on our recent diffusion MRI results.
Peter appropriately commented that the correct way to deal
with anisotropic diffusion was to estimate the full diffusion
tensor , not just the ADC [apparent diffusion constant], as the approach of the time provided.
Basically, ADCs are not sufficient in the presence of
diffusion anisotropy, except in particular cases where the
main diffusion directions coincide with those of the diffusion
MRI measurements. To solve this issue Peter and I came
with a new paradigm, the Diffusion Tensor Imaging (DTI)
framework. By applying simultaneous diffusion-sensitizing
gradient pulses along the X, Y and Z axes the diffusion
MRI signal would become a linear combination of the
diffusion tensor components. From the diffusion MRI signals
acquired along a set of non-colinear directions, encoding
multiple combinations of diffusion tensor components
weighted by the corresponding b values, it would be possible
to retrieve the individual diffusion tensor components at
each location.
In Le Bihan’s Figure 3, he includes a photo of Basser, Jim Mattiello, and himself doing an early diffusion tensor imaging experiment. Le Bihan was the diffusion MRI expert and Mattiello (who worked in the same section as Basser and I did at NIH, and who I’ve written about before) was skilled at writing MRI pulse sequences. When they started collaborating, Basser knew little about magnetic resonance imaging, but he understood linear algebra and its relationship to anisotropy, and realized that by making the “b vector” a matrix he could obtain important information (such as its eigenvalues and eigenvectors) that would determine the fiber direction.
Denis Le Bihon (left), Peter Basser (center) and Jim Mattiello (seated), circa 1991.
Diffusion MRI works because spins that are excited by a radiofrequency pulse will then diffuse away from the tissue voxel being imaged, degrading the signal. The degradation is exponential and given by e–bD, where D is the diffusion constant and b is the “b-factor” that depends on the magnetic field gradient used to extract the diffusion information and the timing of the gradient pulse. I had always thought that this notation went way back in the MRI literature, but according to Le Bihon’s article he named the “b-factor” after himself (“B”ihon)!
Le Bihon describes how the clinical importance of diffusion MRI was demonstrated in 1990 when it was found that stroke victims showed a big change in the diffusion signal while having little change in the traditional magnetic resonance image. In fact, Le Bihon claims that the other big advance in MRI of that era—the development of functional MRI based on the blood oxygenation level dependent (BOLD) imaging—has not yet led to any clinical applications, while diffusion imaging has several.
Le Bihon’s article concludes
Diffusion MRI, as its additions, DTI and IVIM [IntraVoxel Incoherent Motion] MRI, has
become a pillar of modern medical imaging with broad
applications in both clinical and research settings, providing
insights into tissue integrity and structural abnormalities. It
allows to detect early changes in tissues that may not be visible
with other imaging modalities. Diffusion imaging first
revolutionized the management of acute cerebral ischemia
by allowing diagnosis at an acute stage when therapies can
still work, saving the outcomes of many patients. Diffusion
imaging is today extensively used not only in neurology
but also in oncology throughout the body for detecting and
classifying various kinds of cancers, as well as monitoring
treatment response at an early stage. The second major
impact of diffusion imaging concerns the wiring of the brain,
allowing to obtain non-invasively images in 3 dimensions of
the brain connections. DTI has opened up new avenues of
clinical diagnosis and research to investigate brain diseases,
revealing for the first time how defects in white-matter track
integrity could be linked to mental illnesses.
If you want to learn more about diffusion MRI, I recommend Le Bihon’s article. It provides an excellent introduction to the subject, with a fascinating historical perspective.
Dr. Katelyn Jetelina is the founder of “Your Local Epidemiologist,” a public health newsletter that reaches nearly 300,000 people in over 130 countries. Jetelina has a masters in public health and a PhD in epidemiology and biostatistics. She says that her “main goal is to translate the ever-evolving public health science so that people will be well-equipped to make evidence-based decisions.” This year she was named one of TIME magazine’s most influential people in health (that’s how I found out about her). You can find her website at https://yourlocalepidemiologist.substack.com.
These two science communicators gain their credibility because they read, understand, and can explain the scientific literature. Their views usually reflect the scientific and medical consensus. Another way to learn about that consensus is from various scientific and medical professional organizations.
Jetelina and Love both try to reach readers and listeners who may have legitimate questions and concerns about public health controversies. I admire this, and since the election I keep telling myself to be like Katelyn and Andrea; don’t be consumed by frustration and fury, and don’t attack those who disagree with you. But then I compose something like this blog post and I find myself writing with anger and hate. I guess I need both their newsletters to keep me from boiling over, and to serve as examples of how to discuss complex topics rationally.
I follow both Jetelina and Love on Twitter (I refuse to call it “X”). But during the presidential campaign I found Twitter to be a cesspool. I’ve been staying off social media since election day (except, of course, to publish my weekly blog post on Facebook). I’m thinking about deleting my Twitter account, but I’ll probably return to Facebook eventually. I haven’t yet gathered the courage to watch the evening news. I just can’t stomach it. I’m self-medicating by reading P. G. Wodehouse stories, and I’m trying to address my anger management issues. It isn’t easy.
I worked at the National Institutes of Health for seven years. It’s a wonderful institution, which I have tremendous respect for. It pains me to even hint that they might not be the most trustworthy source of health information available. But as I look to the future, I just don’t know. Let’s hope for the best and prepare for the worst by subscribing to Jetelina’s and Love’s newsletters. And in these difficult times I can offer you one bit of good news: both newsletters are free!
For those who want a little more physics mixed in with your public health (and who doesn’t?), I recommend my blog (hobbieroth.blogspot.com) associated with my textbook Intermediate Physics for Medicine and Biology, and my book Are Electromagnetic Fields Making Me Ill? (the answer to the title question is no!). I will do my best to give you the truth, but with the storm clouds I see on the horizon I can’t promise I’ll always give it to you cheerfully. I do promise to delete the profanity before I publish any posts.
A conversation with Dr. Katelyn Jetelina about her journey in the field of epidemiology.
The IOMP held a poster design contest to celebrate the event. The winning poster was created by Lavanya Murugan from Rajiv Gandhi Government General Hospital and Madras Medical College in Chennai, India. IDMP coordinator Ibrahim Duhaini (who works right here in Michigan at Wayne State University) wrote that “Her artwork beautifully captures the theme and spirit of this year’s IDMP and will continuously serve as an inspiration to others… Let us all commit to being beacons of inspiration for the next generation.” I couldn’t have said it better (but maybe Randy Travis could).
The award-winning poster, a masterpiece, is shown below. In case you can’t read it, the quote in the center is by Curie: “Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.” Never has this quote been more relevant than now, as we face the dire health threats generated by climate change. I can identify many of the famous physicists and medical physicists in the poster. Can you? By the way, that little sticky note on the upper left of the frame contains a conversion factor indicating that one roentgen deposits 0.877 rads in dry air.
The winning poster of the design contest associated with the International Day of Medical Physics 2024.
Lavanya sent me her thoughts about the design of the poster.
Inspiration: Once, I gave up my dream of becoming an artist to pursue a career in Medical Physics.
This piece of art is a reflection of my study wall and myself, inspired by the world around me.
Technique: It’s a digital Art piece.
This artwork portrays a young girl immersed in her studies, surrounded by images of great scientists who have contributed to the field of radiation. The wall features news clips about Roentgen’s groundbreaking discovery and a picture of Marie Curie’s notebook, symbolizing power of radiating knowledge.
Everyone experiences uncertainty about their knowledge, future and career at some point. Believing in ourselves is the first step to achieving our goals. The individuals whose photos adorn the wall were once in our shoes, grappling with doubts and questioning their abilities. Yet, they persevered, never giving up and ultimately inspiring us in the field of radiation. Today, we proudly serve healthcare and humanity as Medical Physicists, standing on their shoulders.
I have included one of my favourite quotes from Marie Curie, a female scientist who has been inspiring women in research: “Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.”
Everyone fears radiation and its impact on mankind, but people like us choose to be radiation professionals regardless of the risks involved. This quote inspires us to understand the risks for the betterment of this field.
The message I wanted to convey through this art is to inspire the next generation of Medical Physicists to contribute their best to our field, following in the footsteps of the great minds of our past.
Lavanya is a medical physicist with over eight years of clinical experience in radiotherapy, nuclear medicine, and radiology. She excels in treatment planning, quality assurance, and treatment delivery. She’s also an artist, creating artwork under the pseudonym “Nivi.” You can find many of her pieces at her Instagram account. Below I show a few that are related to medical physics.
Lavanya calls this a “boredom doodle.” You can see a tiny version of it to the right of the Curie quote in her award winning poster.
I am an emeritus professor of physics at Oakland University, and coauthor of the textbook Intermediate Physics for Medicine and Biology. The purpose of this blog is specifically to support and promote my textbook, and in general to illustrate applications of physics to medicine and biology.
