Posts Tagged ‘health’
“To sleep: perchance to dream: ay, there’s the rub”*…
I’m not the first person to note that our understanding of ourselves and our society is heavily influenced by technological change – think of how we analogized biological and social functions to clockwork, then steam engines, then computers.
I used to think that this was just a way of understanding how we get stuff hilariously wrong – think of Taylor’s Scientific Management, how its grounding in mechanical systems inflicted such cruelty on workers whom Taylor demanded ape those mechanisms.
But just as interesting is how our technological metaphors illuminate our understanding of ourselves and our society: because there ARE ways in which clockwork, steam power and digital computers resemble bodies and social structures.
Any lens that brings either into sharper focus opens the possibility of making our lives better, sometimes much better.
Bodies and societies are important, poorly understood and deeply mysterious.
Take sleep. Sleep is very weird.
Once a day, we fall unconscious. We are largely paralyzed, insensate, vulnerable, and we spend hours and hours having incredibly bizarre hallucinations, most of which we can’t remember upon waking. That is (objectively) super weird.
But sleep is nearly universal in the animal kingdom, and dreaming is incredibly common too. A lot of different models have been proposed to explain our nightly hallucinatory comas, and while they had some explanatory power, they also had glaring deficits.
Thankfully, we’ve got a new hot technology to provide a new metaphor for dreaming: machine learning through deep neural networks.
DNNs, of course, are a machine learning technique that comes from our theories about how animal learning works at a biological, neural level.
So perhaps it’s unsurprising that DNN – based on how we think brains work – has stimulated new hypotheses on how brains work!
Erik P Hoel is a Tufts University neuroscientist. He’s a proponent of something called the Overfitted Brain Hypothesis (OBH).
To understand OBH, you first have to understand how overfitting works in machine learning: “overfitting” is what happens when a statistical model overgeneralizes.
For example, if Tinder photos of queer men are highly correlated with a certain camera angle, then a researcher might claim to have trained a “gaydar model” that “can predict sexual orientation from faces.”
That’s overfitting (and researchers who do this are assholes).
Overfitting is a big problem in ML: if all the training pics of Republicans come from rallies in Phoenix, the model might decide that suntans are correlated with Republican politics – and then make bad guesses about the politics of subjects in photos from LA or Miami.
To combat overfitting, ML researchers sometimes inject noise into the training data, as an effort to break up these spurious correlations.
And that’s what Hoel thinks are brains are doing while we sleep: injecting noisy “training data” into our conceptions of the universe so we aren’t led astray by overgeneralization.
Overfitting is a real problem for people (another word for “overfitting” is “prejudice”)…
Sleeping, dreaming, and the importance of a nightly dose of irrationality– Corey Doctorow (@doctorow) explains: “Dreaming and overfitting,” from his ever-illuminating newsletter, Pluralistic. Eminently worthy of reading in full.
(Image above: Gontzal García del Caño, CC BY-NC-SA, modified)
* Shakespeare, Hamlet
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As we nod off, we might send fully-oxygenated birthday greetings to Corneille Jean François Heymans; he was born on this date in 1892. A physiologist, he won the Nobel Prize for Physiology or Medicine in 1938 for showing how blood pressure and the oxygen content of the blood are measured by the body and transmitted to the brain via the nerves and not by the blood itself, as had previously been believed.
“What is research but a blind date with knowledge?”*…
Science at work: a fascinating interactive visualization of every paper ever published in Nature.
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As we interpret influence, we might spare a thought for Neil Arnott; he died on this date in 1874. A physician and inventor, he created one of the first forms of the waterbed, the Arnott waterbed for the comfort of patients during prolonged illness. He also invented the economical Arnott stove (which he called a thermometer-stove), which featured a self-regulating fire. And in 1852, he won the Rumford Medal for the construction of the smokeless fire grate, as well as other improvements to ventilation and heating.
“You’re mugging old ladies every bit as much if you pinch their pension fund”*…
Who benefits from the commercial biomedical research and development (R&D)? Patients-consumers and investors-shareholders have traditionally been viewed as two distinct groups with conflicting interests: shareholders seek maximum profits, patients – maximum clinical benefit. However, what happens when patients are the shareholders?…
Adding investments by governmentally-mandated retirement schemes, central and promotional banks, and sovereign wealth funds to tax-derived governmental financing shows that the majority of biomedical R&D funding is public in origin. Despite this, even in the high-income countries patients can be denied access to effective treatments due to their high cost. Since these costs are set by the drug development firms that are owned in substantial part by the retirement accounts of said patients, the complex financial architecture of biomedical R&D may be inconsistent with the objectives of the ultimate beneficiaries…
It has been estimated that of the total $265 billion spent annually on biomedical research worldwide, over a third – $103 billion comes from public sources. Nevertheless, as public input capital is allocated predominantly into early stage research, nearly all output – medicines – is ultimately brought to the market by private firms. Importantly, these firms are not independent agents. They have owners-shareholders to report to. Until the end of the previous century the major type of owners-shareholders were individual households. At the turn of the millennium, however, they have been displaced by institutional investors, the largest of which are public retirements schemes or quasi-public funds, such as occupational pensions.
First, government money underwrites the basic R&D that goes into drug discovery and development, then public pension monies fund the private companies that bring those drugs to market. As the private companies are solving for highest profits, as opposed to optimal public health, those drugs are often priced out of the reach of the very people whose pension contributions funded their development. Drugs “priced out of reach” is certainly not a new phenomenon; AIDS drugs (to take one example) were priced by Western pharma companies at prices that rendered them inaccessible to most citizens of low-income countries in Africa and Asia. The pensioners in wealthy nations were, effectively, living off of the misery of those in poorer companies.
But the dynamic has continued, deepened– and come home to roost. Now patients in high-income countries are denied access to effective treatments due to their high cost, while these costs are being set by the drug development firms, owned in substantial part by the retirement accounts of those same patients, and benefiting from direct and indirect governmental support.
Investing in one’s own misery– the painful irony of pharma funding: “Pension and state funds dominating biomedical R&D investment: fiduciary duty and public health.”
[Image above: source]
* Ben Elton, Meltdown
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As we untangle unintended consequences, we might send healthy birthday greetings to Charles Value Chapin; he was born on this date in 1856. A physician and epidemiologist, he was a pioneer in American public health. He co-founded in first bacteriological laboratory in the U.S. (in 1888) in Providence, were he was Superintendent of Health– a position he held for 48 years. In 1910, he established Providence City Hospital where infectious disease carriers could be isolated under aseptic nursing conditions; his success inspired similar health control measures throughout the U.S. A professor (at Brown) and prolific writer, his impact on health policy and practice was so broad that he was hailed as “the Dean of City Public Health Officials.”
“Gain not base gains; base gains are the same as losses”*…
When inventor Frederick Banting discovered insulin in 1921, he refused to put his name on the patent. He felt it was unethical for a doctor to profit from a discovery that would save lives. Banting’s co-inventors, James Collip and Charles Best, sold the insulin patent to the University of Toronto for a mere $1. They wanted everyone who needed their medication to be able to afford it. [see here]
Today, Banting and his colleagues would be spinning in their graves: Their drug, which many of the 30 million Americans with diabetes rely on, has become the poster child for pharmaceutical price gouging.
The cost of the four most popular types of insulin has tripled over the past decade, and the out-of-pocket prescription costs patients now face have doubled. By 2016, the average price per month rose to $450 — and costs continue to rise, so much so that as many as one in four people with diabetes are now skimping on or skipping lifesaving doses…
Why Americans ration a drug discovered– and given free to the world– in the 1920s: “The absurdly high cost of insulin, explained.”
* Hesiod (See also Proverbs 28:20: “he that maketh haste to be rich shall not be innocent”)
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As we ponder pleonexia, we might send healing birthday greetings to Edward Lawrie Tatum; he was born on this date in 1909. A geneticist, he shared half of the Nobel Prize in Physiology or Medicine in 1958 with George Beadle for showing that genes control individual steps in metabolism. During World War II, his work was of use in maximizing penicillin production, and it has also made possible the introduction of new methods for assaying vitamins and amino acids in foods and tissues. Tatum and Joshua Lederberg (the winner of the other half of the 1958 Nobel award), discovered genetic recombination in bacteria.
His discoveries were made freely available to the scientific community.
“Don’t be afraid to break things. Don’t be romantic. Don’t take the time to breathe. Don’t aim for perfect. And whatever you do, keep moving.”*…
Eric Feigl-Ding picked up his phone on the first ring. “Busy,” he said, when asked how things were going. He had just finished up an “epic, long” social media thread, he added — one of hundreds he’s posted about society’s ongoing battle with the coronavirus. “There’s so many different debates in the world of masking and herd immunity and reinfection,” he explained, among other dimensions of the pandemic. “We at FAS, we’ve been kind of monitoring all the debates and how we’re seeing signals in which the data goes one way, the debate goes the other,” he said, referring to his work with the Federation of American Scientists, a nonprofit policy think tank. He rattled off a rapid-fire sampler of hot-button Covid-19 topics: the growing anti-vaxxer movement, SARS-CoV-2 reinfection and antibodies, the body of research suggesting masks could decrease viral load, along with a quick mention of the debate among experts about what “airborne” means.
This whirlwind tour through viral Covid-19 themes felt like the conversational equivalent of Feigl-Ding’s Twitter account, which has grown by orders of magnitude since the dawn of the pandemic. The Harvard-trained scientist and 2018 Congressional aspirant posts dozens of times daily, often in the form of long, numbered threads. He’s fond of emojis, caps lock, and bombastic phrases. The first words of his very first viral tweet were “HOLY MOTHER OF GOD.”
Made in January, weeks before the massive shutdowns that brought U.S. society to a halt, that exclamation preceded his observation that the “R0” (pronounced “R-naught”) of the novel coronavirus — a mathematical measure of a disease’s reproduction rate — was 3.8. That figure had been proposed in a scientific paper, posted online ahead of peer review, that Feigl-Ding called “thermonuclear pandemic level bad.” Further in that same Twitter thread, he claimed that the novel coronavirus could spread nearly eight times faster than SARS.
The thread was widely criticized by infectious disease experts and science journalists as needlessly fear-mongering and misleading, and the researchers behind the pre-print had already tweeted that they’d lowered their estimate to an R0 of 2.5, meaning that Feigl-Ding’s SARS figure was incorrect. (Because R0 is an average measure of a virus’s transmissibility, estimates vary widely based on factors like local policy and population density; as a result, researchers have suggested that other variables may be of more use.) He soon deleted the tweet — but his influence has only grown.
At the beginning of the pandemic, before he began sounding the alarm on Covid-19’s seriousness, Feigl-Ding had around 2,000 followers. That number has since swelled to over a quarter million, as Twitter users and the mainstream media turn to Feigl-Ding as an expert source, often pointing to his pedigree as a Harvard-trained epidemiologist. And he has earned the attention of some influential people. These include Ali Nouri, the president of FAS, who brought Feigl-Ding into his organization as a senior fellow; the journalist David Wallace-Wells, who meditated on Feigl-Ding’s “holy mother of God” tweet in his March essay arguing that alarmism can be a useful tool; and former acting administrator of the Centers for Medicare and Medicaid Services Andy Slavitt. (“We all learn so much from you,” he tweeted at Feigl-Ding in July.) Ronald Gunzburger, senior adviser to Maryland Gov. Larry Hogan, even wrote a letter to Feigl-Ding attesting to how his “intentionally provocative tweet” in January “elevated the SARS-CoV-2 virus to the top of our priorities list.”
But as Feigl-Ding’s influence has grown, so have the voices of his critics, many of them fellow scientists who have expressed ongoing concern over his tweets, which they say are often unnecessarily alarmist, misleading, or sometimes just plain wrong. “Science misinformation is a huge problem right now — I think we can all appreciate it — [and] he’s a constant source of it,” said Saskia Popescu, an infectious disease epidemiologist at George Mason University and the University of Arizona who serves on FAS’ Covid-19 Rapid Response Taskforce, a separate arm of the organization from Feigl-Ding’s work. Tara Smith, an infectious disease epidemiologist at Kent State University, suggested that Feigl-Ding’s reach means his tweets have the power to be hugely influential. “With as large of a following as he has, when he says something that’s really wrong or misleading, it reverberates throughout the Twittersphere,” she said…
A scientist has gained popularity as Covid’s excitable play-by-play announcer. But some experts want to pull his plug: “Covid’s Cassandra: The Swift, Complicated Rise of Eric Feigl-Ding.”
* Social media “influencer” Gary Vaynerchuk
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As we interrogate influence, we might send bombastic birthday greetings to Ted Knight; he was born on this date in 1923. An actor and comedian, he was well-known as Henry Rush in Too Close for Comfort, and Judge Elihu Smails in Caddyshack; but he is surely most famous for his role as newscaster Ted Baxter on The Mary Tyler Moore Show.
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