For more than three centuries scientists have believed that human sperm swim by swishing their tails in a side-to-side, symmetrical motion. But that’s because we’ve been looking at them with 2D microscopes.
Using state-of-the-art 3D microscopy, a piezoelectric device, and mathematics, researchers in Mexico discovered how sperm really move: They spin, with a wonky asymmetrical wiggle. The researchers reported their discovery today in the journal Science Advances.
“It’s 2020 and we all thought we knew how sperm actually swim, and we couldn’t have been more wrong,” says Hermes Gadêlha, a senior lecturer in the Department of Engineering Mathematics at the University of Bristol. Gadêlha collaborated on the project with colleagues at the Image and Computer Vision Laboratory at the National Autonomous University of Mexico.
The researchers used a high speed camera capable of recording more than 55,000 frames in one second, and a microscope staged with a piezoelectric device that moved the sperm sample up and down. The camera stayed fixed on a focal point, while the piezoelectric device oscillated in the vertical direction at a speed of 3640 µm/second, which is faster than the movement of the sperm. At that speed, the sperm tail appears to not move, allowing the system to gather an image stack at each moment in 3D. The scientists then made sense of the data using mathematics.
It turns out that the sperm tail is “wonky,” Gadêlha says. It wiggles only on one side. What prevents sperm from swimming in circles is the fact that they roll as they swim in such a way that the lop-sided stroke averages out, allowing forward movement. In other words, sperm create symmetry out of asymmetry.
This had fooled researchers previously because computer-assisted semen analysis systems (yes, that’s a thing) use 2D views to look at sperm movement. From that perspective, the sperm tails, or flagellum, look like they’re just swishing back and forth like eels in water.
It was known that the head spun—that was visible—but it was believed that this was connected with the rolling motion of the flagellum in a symmetrical helical wave. But as it turns out, “the head is rotating and the flagellum is rotating, and these two rotations are independently coordinated,” Gadêlha says.
3D microscopy has been around for over a decade, but it took this long to perfect it and to bring in the other pieces—the math and the piezoelectric device—in a cohesive way. “Sperm move really fast, and the problem has always been how to reconstruct something in 3D that is moving at a super fast rate and that is so small,” says Gadêlha.
The new knowledge of sperm motility will certainly impact fertility studies and other biological research. It could also inspire engineering projects. “What the sperm is doing, after all, are computations with its body, without having a brain,” says Gadêlha. And isn’t that an engineer’s dream? Think soft robotics and artificial intelligence.
Of course, as our instrumentation improves, we might find out, once again, that our latest understanding of sperm movement is actually wrong. And that’s how science goes. “We are less wrong than were before,” says Gadêlha. “But we’re more wrong now than we will be in the future.”
As we write these words, several billion people, the majority of the world’s population, are confined to their homes or subject to physical-distancing policies in an attempt to contain one of the worst pandemics of modern times. Economic activity has plummeted, countless people are out of work, and entire industries have ground to a halt.
Quite understandably, a couple of questions are on everyone’s mind: What is the exit strategy? How will we know when it’s safe to implement it?
Around the globe, epidemiologists, statisticians, biologists, and health officials are grappling with these questions. Though engineering perspectives are uncommon in epidemiological modeling, we believe that in this case public officials could greatly benefit from one. Of course, the COVID-19 pandemic isn’t an obvious or typical engineering problem. But in its basic behavior it is an unstable, open-loop system. Left alone, it grows exponentially, as we have all been told repeatedly. However, there’s good news, too: Like many such systems, it can be stabilized effectively and efficiently by applying the principles of control theory, most notably the use of feedback.
Inspired by the important work of epidemiologists and others on the front lines of this global crisis, we have explored how feedback can help stabilize and diminish the rate of propagation of this deadly virus that now literally plagues us. We’ve drawn on proven engineering principles to come up with an approach that would offer policymakers concrete guidance, one that takes into account both medical and socioeconomic considerations. We relied on feedback-based mechanisms to devise a system that would bring the outbreak under control and then adeptly manage the longer-term caseload.
It is during this longer-term phase, the inevitable relaxing of physical distancing that is required for a functioning society, that the strengths of a response grounded in control theory are most crucial. Using one of the widely available computer models of the disease, we tested our proposal and found that it could help officials manage the enormous complexity of trade-offs and unknowns that they will face, while saving perhaps hundreds of thousands of lives.
Our goal here is to share some of our key findings and to engage a community of control experts in this vital and fascinating problem. Together, we can contribute vitally to the international efforts to manage this outbreak.
The COVID-19 pandemic is unlike any other recent disease outbreak for several reasons. One is that its basic reproduction number, or R0 (“R naught”), is relatively high. R0 is an indication of how many people, on average, an infected person will infect during the course of her illness. R0 is not a fixed number, depending as it does on such factors as the density of a community, the general health of its populace, its medical infrastructure and resources, and countless details of the community’s response. But a commonly cited R0 figure for ordinary seasonal influenza is 1.3, whereas a figure calculated for the experience in Wuhan, China, where COVID-19 is understood to have originated, is 2.6 [PDF]. Figures for some outbreaks in Italy range from 2.76 to 3.25 [PDF].
The goal of infectious-disease intervention is reducing the R0 to below 1, because such a value means that new infections are in decline and will eventually reach zero. But with the COVID-19 outbreak, the level of urgency is extraordinarily high due to the disease’s relatively high fatality rate. Fatality rates, too, are quite variable and depend on such factors as age, physical fitness, present pathologies, region, and access to health care. But in general they are much higher for COVID-19 than for ordinary influenza. A surprisingly large percentage of people who contract the disease develop a form of viral pneumonia that sometimes proves fatal. Many of those patients require artificial ventilation, and if their number exceeds the capacity of intensive care units to accommodate them, some number of them, perhaps a majority, will die.
For that reason, enormous worldwide efforts have focused on “flattening the curve” of infections against time. A high, sharp curve indicating a surge of infections in a short time period, as occurred in China, Italy, Spain, and elsewhere, means that the number of serious cases will swamp the ability of hospitals to treat them and result in mass fatalities. So to reduce the peak demand on health care, the first priority must be to bring the caseload under control. Once that’s done, the emphasis shifts to managing a long-term return to normalcy while minimizing both death rates and economic impact.
The two basic approaches to controlling the spread of disease are mitigation, which focuses on slowing but not necessarily stopping the spread, and suppression, which aims to reverse epidemic growth. For mitigation, R0 is reduced but remains greater than 1, while for suppression, R0 is smaller than 1. Both obviously require changing R0. Officials accomplish that by introducing social measures such as restricted travel, home confinement, social distancing, and so on. These restrictions are referred to as nonpharmaceutical interventions, or NPIs. What we are proposing is a systematically designed strategy, based on feedback, to change R0 through modulation of NPIs. In effect, the strategy alternates between suppression and mitigation in order to maintain the spread at a desired level.
It may sound straightforward, but there are many challenges. Some of them arise from the fact that COVID-19 is a very peculiar disease. Despite enormous efforts to characterize the virus, biologists still do not understand why some people experience fairly mild symptoms while others spiral into a massive, uncontrolled immune response and death. And no one can explain why, among fatalities, men predominate. Other mysteries include the disease’s long incubation period—up to 14 days between infection and symptoms—and even the question of whether a person can get re-infected.
These perplexities have helped bog down efforts to deal with the pandemic. As a recent Imperial College research paper [PDF] notes: “There are very large uncertainties around the transmission of this virus, the likely effectiveness of different policies, and the extent to which the population spontaneously adopts risk reducing behaviours.” Consider the long incubation time and apparent spreading of the virus before symptoms are experienced. These undoubtedly contributed to the relatively high R0 values, because people who were infectious continued to interact with others and transmitted the virus without being aware that they were doing so.
This lag before the onset of symptoms corresponds to time delay in control-system theory. It is notorious for introducing oscillations into closed-loop systems, particularly when combined with substantial uncertainty in the model itself.
In addition to delays, there are very significant uncertainties. Testing, for example, has been spotty in some countries, and that inconsistency has obscured the number of actual cases. That, in turn, made it impossible for officials to know the true level of contagion. Even NPIs are not immutable. The extent to which the public is complying with policies is never 100 percent and may not even be knowable with a high degree of accuracy; people may follow directives less strictly over time. Also, health care capacity can go up because of an increase in available beds due to capacity additions, or down because of a decrease due to a natural disaster.
The point is, a pandemic is a dynamic, fast-moving situation, and inadequate local attempts to monitor and control it can be disastrous. In the Spanish flu pandemic of 1918, cities took widely varying approaches to the lockdown and release of their citizens, with wildly varying results. Some recovered straightforwardly, others had rebound spikes larger than the initial outbreak, and still others had multiple outbreaks after the initial lockdown.
A commonly cited proposal for relaxing social-distancing measures is an on-off approach, where some restrictions are lifted when the number of new cases requiring intensive care is below a threshold and are put back into place when it exceeds a certain number. The research paper [PDF] by the Imperial College COVID-19 Response Team showed how such a strategy is “robust to uncertainty in both the reproduction number, R0, and in the severity of the virus” and offers “greater robustness to uncertainty than fixed duration interventions and can be adapted for regional use.”
This on-off approach is an example of the use of feedback, where the feedback variable is the number of cases in hospital intensive care units. A major drawback of this type of on-off control is that it can lead to oscillations—which, if this strategy is too aggressive, may overwhelm the capacity of the health care system to treat serious cases.
A major advantage of feedback here is that it lessens the impact of model uncertainty—meaning that if carefully designed, a strategy can be effective even if the models it is based on are not accurate. We do not yet have an accurate epidemiological model of COVID-19 and will likely not have one for at least several months, if at all. Furthermore, the physical distancing and confinement regimes that have been put into place are new, so we don’t really know yet exactly how effective they’ll be or even the extent to which people are complying with them.
In the absence of widespread immunity or vaccination, the only way to suppress the disease is total confinement—obviously not a viable long-term solution. A reasonable middle ground is to implement a feedback policy designed to keep R0 close to 1, with perhaps small oscillations on either side. In so doing we would maintain the critical caseload within the capacity of health care institutions while slowly and safely building immunity in our communities, and returning to normal social and economic conditions as quickly as is safely possible.
A key point is that the design of the policy be rigorous from a control-engineering viewpoint while remaining comprehensible to epidemiologists, policymakers, and others without deep knowledge of control theory. It should also be capable of generating restriction regimes that can be translated into practical public policies. If the tuning mechanism is too aggressive—for example, switching between full and zero social distancing—it will lead to severe oscillations and overwhelmed hospitals, and very likely frustration and social-distancing fatigue among the people who need to follow dramatically changing rules.
On the other hand, tuning that is too timid also courts fiasco. An example of such tuning might be a policy requiring a full month in which no new cases are recorded before officials relax restrictions. Such a hypercautious approach risks needlessly prolonging the pandemic’s economic devastation, creating a catastrophe of a different sort.
But a properly designed feedback-based policy that takes into account both dynamics and uncertainty can deliver a stable result while keeping the hospitalization rate within a desired approximate range. Furthermore, keeping the rate within such a range for a prolonged period allows a society to slowly and safely increase the percentage of people who have some sort of antibodies to the disease because they have either suffered it or they have been vaccinated—preferably the latter.
Eventually, as the percentage of the population who have suffered the disease and recovered from it becomes high enough, the number of susceptible people becomes small enough that the virus’s rate of spread is naturally lowered. This phenomenon is called herd immunity, and it is how pandemics have generally died out in the past. However, the question of whether or how such immunity to COVID-19 can be built up is still under investigation, which makes it particularly important to monitor and manage nonpharmaceutical interventions as a function of the actual spread of the virus and of hospitalization rates.
In designing our control system, we relied strongly on the fact that most nonpharmaceutical interventions are not binary, on-off quantities but instead can take on a range of values and can be implemented that way by policymakers. For example, stay-at-home directives can be applied disproportionately to specific groups of people who are particularly at risk because of age or a preexisting health condition. Then the number of people who are affected by the directive can be increased or decreased by simply changing the guidelines on who is “at risk.” We can similarly widen or narrow the definition of people who are designated “essential” and are therefore exempt from the directives. Public meetings, too, may be banned for n participants, where the value of n can be increased as things loosen up.
Restrictions on travel, too, can be quite variable. Full lockdown limits people to moving within the boundaries of their property. But as conditions improve, officials might grant access to businesses within a couple, and then perhaps a dozen, kilometers of their home, and so on. These are all obviously important levers.
To explore how such variability can save lives, we devised a series of scenarios, each indicative of a recovery strategy with a different level of feedback, and simulated the resulting policies against a commonly used infectious-disease computer model. We plotted the results in a series of graphs showing COVID-19 hospital cases as a function of time. Hospital occupancy may be a more reliable and tangible measure than total case count, which depends on extensive testing that many countries (such as the United States) do not have at the moment. Furthermore, hospital ICU bed occupancy or ventilator availability is arguably an important measure of the ability of the local health care system to treat those who are suffering from respiratory distress acute enough to require intensive care and perhaps assisted breathing.
The model we used was created by Jeffrey Kantor, professor of chemical engineering at the University of Notre Dame (Kantor’s model is available on GitHub). The model assumes we can suppress disease transmission to a very low level by choosing appropriate policy levers. Although worldwide experience with COVID-19 is still limited, at the time of this writing this assumption appears to be a realistic one.
To make the model more reflective of our current understanding of COVID-19, we added two types of uncertainty. We assumed different values of R0 to see how they affected outcomes. To consider how noncompliance with nonpharmaceutical interventions would affect results, we programmed for a range of effectiveness of these NPIs.
Our first, simplest simulation confirms what we all know by now, which is that not doing anything was not an option [Figure 1].
The large and lengthy peak well above the available bed capacity in the intensive care unit indicates a huge number of cases that will likely result in death. This is why, of course, most countries have put aggressive measures in place to flatten the curve.
So what do we do when the number of infections comes down? As this second simulation clearly shows [Figure 2], relaxing all restrictions when the number of infections has come down will only lead to a second surge in infections. Not only could this second surge overwhelm our hospitals, it could also lead to an even higher mortality rate than the first surge, as occurred repeatedly in several U.S. cities during the Spanish flu epidemic of 1918.
Now let’s consider the simple on-off approach to confinement, in which most of the usual restrictions on gatherings, travel, and social interaction are lifted entirely when the number of new ICU cases drops below a lower threshold, and then are put back into place when this number exceeds a higher threshold. In this case, the R0 swings sharply between two levels, a high above 2 and a low below 1, as shown in blue in the graph [Figure 3]. This approach leads to oscillations, and if it is applied too aggressively, the high points of these oscillations will exceed the health care system’s capacity to treat patients. Another likely problem with this approach has been labeled “social distance fatigue.” People become weary of the repeated changes to their routine—going back to work for a couple of weeks, then being told to stay at home for a few weeks, then given the all-clear to go back to work, and so on.
We can do better. For our third experiment, we developed a scenario in which we targeted 90 percent occupancy of hospital intensive care units. To achieve this, we designed a simple feedback-based policy using the principles of control systems theory.
When R0 is high, many restrictions are put into place. People are largely confined to their homes and services are limited to the bare minimum needed for society to function—utilities, police, sanitation, and food distribution, for example. Then, as conditions begin to improve, as revealed by our feedback measure of hospital-bed occupancy, other services are gradually phased in. Recovered people are allowed to move freely as they can no longer contract, or transmit, the virus. Perhaps people are allowed to visit restaurants within walking distance, some small businesses are allowed to reopen under certain conditions, or certain age groups are subject to less-stringent restrictions. Then geographical mobility might be loosened in other ways. The point is that restrictions are eased gradually, with each new gradation based carefully on feedback.
This strategy results in a stable response that maximizes the rate of recovery. Furthermore, the demand for hospital ICU beds never exceeds a threshold, thanks to a “set point” target below that threshold [Figure 4]. The health care capacity limit is never breached. In addition, note the general upward trend for the release of restrictions, as the number of recovered and immune people grows and nonpharmaceutical interventions are gradually phased out.
This simplified example shows how using feedback to modulate the restrictions imposed on a population to modify R0 leads to a policy that is robust. For example, early on in the outbreak, there will be a great deal of uncertainty about R0 because testing will still be spotty, and because an unknown number of people may have the disease without realizing it. That uncertainty will inevitably fuel a surge in initial cases. However, once the case count is stabilized by the initial restrictive regime, a policy based on feedback will prove very tolerant of variations in R0, as illustrated in Figure 5. As the graph shows, after a few months it doesn’t matter whether the R0 is 2 or 2.6 because the total case count stays well below the number of available hospital beds due to the use of feedback.
As an added benefit, using feedback makes the policy effective even in the face of likely degrees of noncompliance. In practice, what noncompliance means is that a given level of restrictions will result in an R0 that is slightly higher than expected, which in turn causes fluctuations in the number of people who are infected. Noncompliance might, for instance, result in the restrictions being 10 percent less effective than intended [Figure 6]. However, through feedback, the policy will automatically tighten to compensate.
In reality, various factors that the model treats as invariable, such as health care capacity, might be anything but. However, variations of this sort can typically be accommodated in a policy, for example by changing the threshold on ICU occupancy.
Clearly, tried-and-true principles of control theory, particularly feedback, can help officials plot more robust and optimal strategies as they attempt to deal with the devastating COVID-19 pandemic. But how to make officials aware of these powerful tools?
Imagine an online interactive tool offering detailed, specific guidance in plain language and aimed at public officials and others charged with mounting a response to the pandemic in their communities. The guidance would be based on strategies developed by a small group of control theorists, epidemiologists, and people with policy experience. The site could review the now-familiar initial response, in which nonessential workers are confined to their homes except for essential needs. Then the site could go on to give some guidance on how and when the tightest restrictions could be lifted.
The biggest challenge to the designers of this Web-based tool will be enabling nonspecialists to visualize how the various components of the epidemiological model interact with the various feedback policy options and model uncertainty. How exactly should the main feedback measure—likely some aspect of hospital or intensive care occupancy—be implemented? Which restrictions should be lifted in the first round of easing? How should they be eased in the first round? In the second round? While monitoring the feedback measure, how frequently should officials consider whether to implement another round of easing? Feedback will help officials determine when to time various phases of interventions. An interactive tool that could assess different policy approaches, illustrating what conditions must be in place to alleviate uncertainty and shrink the projected caseload, would be very valuable indeed.
Working with political officials, epidemiologists, and others, control engineers can systematically design policies that take these constraints and trade-offs into account. It comes down to this: In the many months of struggle ahead, such a collaboration could save countless lives.
Editor’s note: Material in this article originally appeared as a post on Medium, “Coronavirus: Policy Design for Stable Population Recovery,” and in several other venues.
Greg Stewart is vice president of data science for the agriculture technology startup Ecoation and an adjunct professor at the University of British Columbia, in Vancouver. A Fellow of the IEEE, he has led the research, development, and deployment of control and machine-learning technology in such applications as microalgae cultivation, large-scale data centers, automotive power-train control, and semiconductor fabrication. He is currently developing models and strategies for controlling the spread of pests and disease in agriculture.
Klaske van Heusden is a research associate at the University of British Columbia in Vancouver. An IEEE Senior Member, her research interests include modeling, prediction, and control, with applications in medical devices, mechatronics, and robotics. Lately she has been working on a robust and provably safe automated drug-delivery device for use in operating rooms.
Guy A. Dumont is a professor of electrical and computer engineering at the University of British Columbia in Vancouver and a principal investigator at BC Children’s Hospital Research Institute. An IEEE Fellow, he has 40 years of experience applying advanced control theory in the process industries, in particular pulp and paper and, for the last 20 years, in biomedical applications such as automated drug delivery for closed-loop control of anesthesia.
“Coronavirus: Why You Must Act Now,” Medium, by Tomas Pueyo
“Coronavirus: The Hammer and the Dance,” Medium, by Tomas Pueyo
Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand [PDF], by the Imperial College COVID-19 Response Team
Potential Long-Term Intervention Strategies for COVID-19, GitHub, by Marissa Childs, Morgan Kain, Devin Kirk, Mallory Harris, Jacob Ritchie, Lisa Couper, Isabel Delwel, Nicole Nova, Erin Mordecai
“When Can We Let Up? Health Experts Craft Strategies to Safely Relax Coronavirus Lockdowns,” Stat, by Sharon Begley
“How Some Cities ‘Flattened the Curve’ During the 1918 Flu Pandemic,” National Geographic, by Nina Strochlic and Riley D. Champine
“First-Wave COVID-19 Transmissibility and Severity in China Outside Hubei After Control Measures, and Second-Wave Scenario Planning: A Modelling Impact Assessment,” The Lancet, by Kathy Leung, Joseph T. Wu, Di Liu, Gabriel M. Leung
The U.S. Food and Drug Administration has granted an emergency use authorization for treating suspected COVID-19 patients with a non-invasive vagus nerve stimulator. The handheld device, made by electroCore, in Basking Ridge, N.J., sends a train of electric pulses through the skin to a nerve in the neck. Research has shown this pulse train causes airways in the lungs to open and may also have a more general anti-inflammatory effect.
According to FDA’s authorization, the gammaCore Sapphire CV device can be used either at home or in a clinic or hospital to “acutely treat adult patients with known or suspected COVID-19 who are experiencing exacerbation of asthma-related [shortness of breath] and reduced airflow, and for whom approved drug therapies are not tolerated or provide insufficient symptom relief.”
The vagus nerves run along either side of the neck and connect structures deep in the brain with the body’s internal organs. (See “The Vagus Nerve: A Back Door For Brain Hacking,” June 2015.) Medical device makers have been taking advantage of this brain-organ highway to treat epilepsy, depression, postpartum bleeding, and more. ElectroCore’s device is already approved for both acute and long-term treatment of migraine and cluster headaches.
However, the company was founded on the back of research into the vagus nerve stimulations’ effect on airways, says Dr. Peter Staats, electroCore’s chief medical officer. “Early on, when we were studying airway activity, we asked patients if they experienced anything else,” he recalls. “An early patient said ‘My headache went away.’” Once the company was established, headache became the initial focus. Conveniently, the same set of stimuli used for migraine and headache—two minutes of 25 pulses per second of a 5000 Hz signal—also work for the lungs. “We’ve kind of come full circle,” says Staats.
With respect to the lungs and COVID-19, the device appears to have a two-pronged effect. The first, opening up the end terminals of the lung’s airways, is mediated by signals going up the nerve into the brain, says Staat. The second, a separate anti-inflammatory effect, appears to be caused by signals traveling down the nerve into the body. The working theory is that this second signal has an effect on cytokine production. Cytokines are a broad class of small proteins that cells use to signal to each other, some of which play a role in inflammation. Their overproduction can cause a “cytokine storm syndrome” that’s been seen in some COVID-19 patients, where an immune response spins out of control and can shut down the lungs and other organs.
Under the FDA authorization, the device is only approved for use during the COVID-19 national emergency. However, researchers have several ongoing clinical trials and FDA has invited the company to seek more permanent approval, according to electroCore CEO Dan Goldberg. And he expects the company to continue exploring the use of noninvasive vagus nerve stimulation (nVNS) for other maladies, he says. “Under the pandemic circumstances we’re laser focused, but we continue to believe that nVNS is a broad platform for a variety of clinical indications,” he says.
An astronaut aboard the International Space Station (ISS) has successfully assembled human cartilage using the power of magnetism.
The feat was achieved using a magnetic levitation bioassembly device installed onboard the station. The machine enables clusters of human cells to assemble into tissue structures, without the use of a physical scaffold. The experiment was described in a paper published today in the journal Science Advances.
“One could imagine not too far in the future that if we colonize Mars or do long-term space travel, we might want to do experiments where we build functional tissues in space, and test them in extraterrestrial environments,” says Utkan Demirci, a researcher at Stanford and an author of the paper.
Imagine getting a space-related injury that rips off your skin or bone, and being able to patch it up with bioengineered tissue—like the movie “Ad Astra,” where people live, work and receive medical treatment on the red planet.
Although bioprinters capable of printing biomaterial already exist on Earth, these devices can’t work in space because they rely on the existence of Earth’s relatively strong gravity. “They all rely on a toothpaste-like extrusion that has to come down with gravity so that you can build the next layer,” says Demirci.
Magnetic levitation, however, works just fine in orbit. The method involves suspending an object with no other support other than magnetic force. It can be used to counteract the effects of gravitational acceleration and any other accelerations—and also hold objects in place in the absence of gravity.
Normally, we don’t think of tissues as things that can be moved around by magnetic fields in the way that a piece of metal can be. But Demirci and Naside Gozde Durmus at Stanford in 2015 demonstrated that living cells, too, could be manipulated when placed in a paramagnetic fluid medium. This involves placing two strong, opposing off-the-shelf magnets so close to each other that a high gradient force is generated. A microfluidic channel consisting of the diamagnetic cells and the paramagnetic fluid is placed between the magnets.
The difference between the magnetic susceptibility of the diamagnetic cells and the paramagnetic fluid, multiplied by the gradient of the magnetic field, is enough to balance the weight of the cell, thus levitating it. When groups of cells are subjected to these conditions, they migrate to the same spot in the medium, assembling into 3D tissue structures and organoids.
Vladislav Parfenov at the Laboratory for Biotechnological Research, CD Bioprinting Solutions, in Moscow, and his colleagues, later expanded on the idea by building a device that enables the assembly of groups of cells, called spheroids, into three-dimensional structures. That’s the device that ended up in space.
Getting it to the ISS wasn’t easy. The hardware, which is the shape of a half wheel of cheese, had to be space-flight-ready. “There are space standards for how rugged a machine should be to go out there,” says Demirci. “It can’t be something that’s going to shake and break.” Special cameras had to be installed in the device to record the activity of the cells, since the necessary optical equipment such as microscopes wouldn’t be available on the ISS.
Then, in November 2018, the rocket that was taking it to the ISS blew up. (The two astronauts aboard survived unharmed after separating from the rocket and making a dramatic landing.) Parfenov and team built another bioassembler and got it onto the next rocket out of Kazakhstan that December.
A day after the device’s arrival on the Russian segment of the ISS, cosmonaut Oleg Kononenko performed the experiment, which involved injecting the paramagnetic medium into the cuvettes with the cartilage cells (derived from human knees and hips), cooling them, putting them into the magnetic bioassembler, and pressing go.
This was the first time that cells and organoids have been assembled and biofabricated in space, says Demirci. “People have been doing biological experiments and culturing cells in space, but being able to actually assemble these building blocks into more complex structures using a biomanufacturing tool—that’s a first,” he says.
Such experiments could also aid research on cell interactions that benefit life here on Earth. “In the absence of gravity, cells and proteins behave very differently,” says Demirci. Understanding these interactions without gravitational noise could reveal new information about how drugs and cells interact, he says.
It’s not a cure for COVID-19, but perhaps it’s worth taking a break from the pandemic to think about this cool biomedical tool in space. We could all certainly use a little, ahem, levity, right now.
Confusion and skepticism may confound efforts to make use of digital contact tracing technologies during the COVID-19 pandemic. A recent survey found that just 42 percent of American respondents support using so-called contact tracing apps—an indication of a lack of confidence that could weaken or even derail effective deployment of such technologies.
Most contact tracing apps generally try to collect some form of information about a smartphone user’s encounters with other people and notify those users if they were potentially exposed to a confirmed COVID-19 case. But each app has its own approach to privacy and can differ in whether it collects more specific location data based on GPS or merely records close encounters with other smartphones based on Bluetooth radio-wave transmissions. Those differences, coupled with public misunderstanding of different apps, can make it tricky to assess public opinion of specific digital contact tracing technologies.
“We found that there is variation in terms of how willing people are to download the apps based on the features of the app,” says Baobao Zhang, a Klarman postdoctoral fellow at Cornell University whose research focus is the governance of AI. “There's many different kinds of apps that are out there, so if you're just going to ask about a contact tracing app, people might have very different views of what it does.”
In late April and late June, Zhang and her colleagues conducted two rounds of surveys focused on gauging American opinions of such apps. The results are described in a preprint paper first published on 5 May and later updated on 29 June; the update accounts for an initial problem with the survey software and includes a second round of survey findings.
The contact tracing apps in question build upon traditional contact tracing, but they are not the same. Traditional contact tracing is a tried-and-true public health measure that requires large numbers of human contact tracers to call and interview suspected or confirmed COVID-19 cases about their travel history for the purpose of warning family, friends, or strangers who may have been exposed.
But given how labor- and time-intensive manual contact tracing can be, some governments and companies have looked to digital contact tracing systems to help automate part of the process. These systems can include contact tracing apps, which in their most privacy-preserving form may be more accurately described as exposure notification apps. They function primarily to alert individual smartphone users rather than public health officials or human contact tracing teams.
Zhang and her colleagues used conjoint analysis to try gauging how Americans valued or viewed certain features of such apps. For example, app designs can can choose whether to collect GPS location data or to rely primarily upon Bluetooth key code exchanges. Whereas a GPS-based app might notify the user about their potential exposure to COVID-19 at a particular location—information that could also help jog fuzzy human memories—the Bluetooth-based app would typically only tell users that they had potentially been exposed to someone for perhaps a certain amount of time.
A GPS-based app is “probably is more effective from a public health standpoint,” says Sarah Kreps, professor of government and adjunct professor of law at Cornell University and coauthor of the survey research paper. But Kreps adds that the same GPS location data is “very intrusive from a privacy standpoint” because the information could reveal behavioral and lifestyle patterns about a person’s daily life.
Americans who took the survey did not seem to view apps very differently based on whether they incorporated GPS or Bluetooth. But respondents did change their minds when it came to whether an app featured a centralized vs. decentralized system of data storage. The centralized system shares much more information—such as a user’s anonymized ID and Bluetooth key codes—with a central server that might be overseen by a company or government agency. The decentralized system typically stores most of the collected data on individual phones in the interest of better protecting user privacy.
“We found that the decentralized data storage in the contact track tracing app increases people's willingness to download it,” Zhang says.
But the survey research also suggests that it’s easy for people to get confused about which app does what despite the survey’s best attempt to also educate people about different app features. For example, the survey took time up front to explain that such apps would not identify anyone by name. Still, a later question showed that 30 percent of respondents believed the apps would identify infected people by name and share those names with smartphone users who might have been exposed to them.
“What's interesting in our study is that even after informing respondents about how these apps work, we did a manipulation check to see if people understood and they don't always get it right,” Zhang says. “So in terms of public education, I think there's a lot more work to be done to correct some of the misinformation about these apps.”
There is certainly no shortage of confusion about digital contact tracing efforts. One prominent example is the Google and Apple Exposure Notification (GAEN) system. GAEN makes it easier for third-party developers to create apps that harness Bluetooth capabilities in both Android and iOS devices to exchange randomly-generated IDs whenever phone users are in relatively close proximity. Some countries have already built and deployed such apps based on the GAEN framework, but the handful of U.S. efforts attempting to do so have not yet been rolled out to the public.
The tech giants also worked to enable GAEN at an operating system level so that individuals can go into their smartphone system settings and choose to opt-in for receiving Bluetooth beacon notifications about having been in close proximity to a confirmed COVID-19 case who was using a GAEN-compatible app. If they hadn’t downloaded a GAEN-compatible app already, the notified users would then be prompted to download such an app to get more information.
But some smartphone users became alarmed when that option appeared in the system settings of their Android and iOS devices as part of routine software updates in June. Zhang recalled friends calling her and asking about whether Google or Apple had installed an app on their phones that was tracking them somehow. In reality, the GAEN system would not share an anonymized individual’s health status unless that person chose to opt-in via their phone’s system settings, downloaded a compatible app, and then manually entered the fact that they had tested positive for COVID-19 into the app.
"It was sort of this like shadowy feature of the [operating system], that I think because it didn't the accompany the actual app, there was almost this suspicion that something was operating in the background without people knowing about it,” Kreps said.
The public reaction may have something to do with survey results showing that just 35 percent of Americans felt that Google and Apple should automatically install such an app on their phones through a software update. The GAEN system roll-out to Android and iOS devices did not automatically install such an app, but the distinction seems to have been lost on many people.
Furthermore, the survey research found no significant difference in people’s willingness to download an app based on whether it was developed by the Silicon Valley tech giants Apple and Google, by the U.S. Centers for Disease Control and Prevention (CDC), by a state government, or by university researchers. By comparison, an online survey commissioned by the software security company Avira found that American respondents tended to trust Apple and Google more than the government on contact tracing apps, even as overall support for contact tracing apps remained low.
“That's just a sort of public health disaster, because those kinds of episodes really undermine the trust that is necessary from the public for these kinds of apps to work,” Kreps says.
Data compiled by MIT Technology Review on national efforts to deploy contact tracing apps suggests that most have failed to gain traction among even a simple majority of their citizens. But Americans might feel more confident in exposure notification apps if the U.S. enacts more national and state laws that clearly protect individual privacy rights. In that spirit, U.S. lawmakers in Congress have introduced several bills that propose to regulate the health data collected by such apps and similar digital contact tracing technologies.
One of the more narrowly-focused examples is a bipartisan bill named the The Exposure Notification Privacy Act (PDF) that was introduced in the U.S. Senate. “It makes sure that the app is voluntary and it prohibits app developers from using the data collected by the app for commercial purposes, so I think that's moving in the right direction,” Zhang explains. But it’s unclear if such proposals can gain traction while U.S. local and state governments grapple with fresh COVID-19 outbreaks in the wake of attempts to reopen businesses.
The survey suggests that American support for contact tracing apps, which sits at 42 percent, puts it squarely behind approval ratings for several other public health surveillance measures—notably traditional contact tracing and temperature checks. (Both of those measures received the backing of more than 50 percent of respondents.) But public opinion is not necessarily set in stone, if some of the evolving views reflected in the two survey rounds are any indication.
“Temperature checks weren't ranked number one in terms of public support last time and now it is,” Zhang says. “Maybe because it's become more common, people are more accepting of it.”
The survey also found that political affiliation plays a role in American views on some public health surveillance measures. Overall, more Democrats tended to favor many of those measures compared with their Republican counterparts. But the study found no significant partisan difference in support for contact tracing apps. That may hint at an opportunity for a bipartisan push to help Americans better understand and potentially try such apps, the researchers suggest.
”Who would have thought the masks would be politicized?” Kreps says. “But it suggests that probably everything eventually will be,” she predicts. “Given the partisan polarization in the political landscape, it suggests that if [digital contact tracing] is going to be successful, public health authorities might want to get out in front of it to depoliticize it to the extent possible.”
Researchers have been banking on millions of citizen-scientists around the world to help identify new treatments for COVID-19. Much of that work is being done through distributed computing projects that utilize the surplus processing power of PCs to carry out various compute-intensive tasks.
One such project is Folding@home, which helped model how the spike protein of SARS-CoV-2 binds with the ACE2 receptor of human cells to cause infection. Started at Stanford University in 2000, Folding@home is currently based at the Washington University School of Medicine in St. Louis; it undertakes research into various cancers, and neurological and infectious diseases by studying the movement of proteins.
Proteins are made up of a sequence of amino acids that fold into specific structural forms. A protein’s shape is critical in its ability to undertake its specific function. Viruses have proteins that enable them to suppress a host’s immune system, invade cells, and replicate.
Greg Bowman, director of Folding@home, says, “We’re basically building maps of what these viral proteins can do… [The distributed computing network] is like having people around the globe jump in their cars and drive around their local neighborhoods and send us back their GPS coordinates at regular intervals. If we can develop detailed maps of these important viral proteins, we can identify the best drug compounds or antibodies to interfere with the virus and its ability to infect and spread.”
After Covid-19 was declared a global pandemic, Folding@home prioritized research related to the new virus. The number of devices running its software shot up from some 30,000 to over 4 million as a result. Tech behemoths such as Microsoft, Amazon, AMD, Cisco, and others have loaned computing power to Folding@home. The European Organization for Nuclear Research (CERN) has freed up 10,000 CPU cores to add to the project, and the Spanish premier soccer league La Liga has chipped in with its supercomputer that is otherwise dedicated to fighting piracy.
While Folding@home models how proteins fold, another distributed computing project called Rosetta@home—this one at the University of Washington Institute for Protein Design (IPD)—predicts the final folded shape of the protein. Though the projects are quite different, they are complementary.
“A big difference…is that the Rosetta@home distributed computing is…directly contributing to the design of new proteins… These calculations are trying to craft brand new proteins with new functions,” says Ian C. Haydon, science communications manager and former researcher at IPD. He adds that the Rosetta@home community, which comprises about 3.3 million instances of the software, has helped the research team come up with more than 2 million candidate antiviral proteins that recognize the coronavirus’s spike protein and bind very tightly to it. When that happens, the spike is no longer able to recognize or infect a human cell.
“At this point, we’ve tested more than 100,000 of what we think are the most promising options,” Haydon says. “We’re working with collaborators who were able to show that the best of these antiviral proteins…do keep the coronavirus from being able to infect human cells…. [What’s more,] they have a potency that looks at least as good if not better than the best known antibodies.”
There are many possible outcomes for this line of research, Haydon says. “Probably the fastest thing that could emerge… [is a] diagnostic…tool that would let you detect whether or not the virus is present.” Since this doesn’t have to go into a human body, the testing and approval process is likely to be quicker. “These proteins could [also] become a therapy that…slows down or blocks the virus from being able to replicate once it’s already in the human body… They may even be useful as prophylactic.”