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Retinal waves prime visual motion detection by simulating future optic flow

Science

As a mouse runs forward across the forest floor, the scenery that it passes flows backwards. Ge et al. show that the developing mouse retina practices in advance for what the eyes must later process as the mouse moves. Spontaneous waves of retinal activity flow in the same pattern as would be produced days later by actual movement through the environment. This patterned, spontaneous activity refines the responsiveness of cells in the brain's superior colliculus, which receives neural signals from the retina to process directional information. Science , abd0830, this issue p. [eabd0830][1] ### INTRODUCTION Fundamental circuit features of the mouse visual system emerge before the onset of vision, allowing the mouse to perceive objects and detect visual motion immediately upon eye opening. How the mouse visual system achieves self-organization by the time of eye opening without structured external sensory input is not well understood. In the absence of sensory drive, the developing retina generates spontaneous activity in the form of propagating waves. Past work has shown that spontaneous retinal waves provide the correlated activity necessary to refine the development of gross topographic maps in downstream visual areas, such as retinotopy and eye-specific segregation, but it is unclear whether waves also convey information that instructs the development of higher-order visual response properties, such as direction selectivity, at eye opening. ### RATIONALE Spontaneous retinal waves exhibit stereotyped changing spatiotemporal patterns throughout development. To characterize the spatiotemporal properties of waves during development, we used one-photon wide-field calcium imaging of retinal axons projecting to the superior colliculus in awake neonatal mice. We identified a consistent propagation bias that occurred during a transient developmental window shortly before eye opening. Using quantitative analysis, we investigated whether the directionally biased retinal waves conveyed ethological information relevant to future visual inputs. To understand the origin of directional retinal waves, we used pharmacological, optogenetic, and genetic strategies to identify the retinal circuitry underlying the propagation bias. Finally, to evaluate the role of directional retinal waves in visual system development, we used pharmacological and genetic strategies to chronically manipulate wave directionality and used two-photon calcium imaging to measure responses to visual motion in the midbrain superior colliculus immediately after eye opening. ### RESULTS We found that spontaneous retinal waves in mice exhibit a distinct propagation bias in the temporal-to-nasal direction during a transient window of development (postnatal day 8 to day 11). The spatial geometry of directional wave flow aligns strongly with the optic flow pattern generated by forward self-motion, a dominant natural optic flow pattern after eye opening. We identified an intrinsic asymmetry in the retinal circuit that enforced the wave propagation bias involving the same circuit elements necessary for motion detection in the adult retina, specifically asymmetric inhibition from starburst amacrine cells through γ-aminobutyric acid type A (GABAA) receptors. Finally, manipulation of directional retinal waves, through either the chronic delivery of gabazine to block GABAergic inhibition or the starburst amacrine cell–specific mutation of the FRMD7 gene, impaired the development of responses to visual motion in superior colliculus neurons downstream of the retina. ### CONCLUSION Our results show that spontaneous activity in the developing retina prior to vision onset is structured to convey essential information for the development of visual response properties before the onset of visual experience. Spontaneous retinal waves simulate future optic flow patterns produced by forward motion through space, due to an asymmetric retinal circuit that has an evolutionarily conserved link with motion detection circuitry in the mature retina. Furthermore, the ethologically relevant information relayed by directional retinal waves enhances the development of higher-order visual function in the downstream visual system prior to eye opening. These findings provide insight into the activity-dependent mechanisms that regulate the self-organization of brain circuits before sensory experience begins. ![Figure][2] Origin and function of directional retinal waves. ( A ) Imaging of retinal axon activity reveals a propagation bias in spontaneous retinal waves (scale bar, 500 μm). ( B ) Cartoon depiction of wave flow vectors projected onto visual space. Vectors (black arrows) align with the optic flow pattern (red arrows) generated by forward self-motion. ( C ) Asymmetric GABAergic inhibition in the retina mediates wave directionality. ( D ) Developmental manipulation of wave directionality disrupts direction-selective responses in downstream superior colliculus neurons at eye opening. The ability to perceive and respond to environmental stimuli emerges in the absence of sensory experience. Spontaneous retinal activity prior to eye opening guides the refinement of retinotopy and eye-specific segregation in mammals, but its role in the development of higher-order visual response properties remains unclear. Here, we describe a transient window in neonatal mouse development during which the spatial propagation of spontaneous retinal waves resembles the optic flow pattern generated by forward self-motion. We show that wave directionality requires the same circuit components that form the adult direction-selective retinal circuit and that chronic disruption of wave directionality alters the development of direction-selective responses of superior colliculus neurons. These data demonstrate how the developing visual system patterns spontaneous activity to simulate ethologically relevant features of the external world and thereby instruct self-organization. [1]: /lookup/doi/10.1126/science.abd0830 [2]: pending:yes


News at a glance

Science

SCI COMMUN### Astronomy The Hubble Space Telescope ended a monthlong hiatus on 16 July when operators successfully switched a failed control system to backup devices. The trouble started on 13 June when Hubble's payload computer, which controls its instruments, halted, and the main spacecraft computer put all the astronomical instruments in safe mode. Operators were unable to restart the payload computer, and switching memory modules—which they initially thought were at fault—didn't wake the telescope. They tested and ruled out problems in other devices before zeroing in on a power control unit. NASA called in retired staff to help devise a fix for the 31-year-old telescope, which involved remotely switching to a spare power control unit and other backup hardware for managing the instruments and their data. The agency practiced and checked the repair on the ground for 2 weeks before executing it. After powering up all the hardware, Hubble returned to work on 17 July, and has already beamed back new images. NASA says it expects Hubble to continue for many years. ### Conservation A new automated alert system can help veterinarians get a jump on investigating disease outbreaks and disasters afflicting wildlife. Researchers at the University of California, Davis, and colleagues used a machine learning algorithm to scan case reports of sick and dead wildlife submitted to a database by wildlife clinics and rehabilitation centers in the United States and other countries. The researchers used data from 3081 reports filed from California to train the algorithm to detect patterns of species suffering common symptoms. The software is designed to identify unusual events in one of 12 clinical categories, such as mass starvation or an oil spill. The algorithm assigned the correct category to 83% of cases examined, including ones from an outbreak of neurological disease in California brown pelicans (above) and red-throated loons, the research team reported last week in the Proceedings of the Royal Society B . The system could help wildlife officials more quickly detect developing problems and confirm specific causes. ### Public health Reflecting another toll of the coronavirus pandemic, 23 million children missed routine vaccinations in 2020, the most since 2009 and 19% more than in 2019, the World Health Organization (WHO) and UNICEF said last week. As many as 17 million didn't receive any childhood vaccine at all. The pandemic led to closures or cutbacks at vaccination clinics and lockdowns that prevented parents and their children from reaching them, the groups reported. In addition, 57 mass vaccination campaigns for non–COVID-19 diseases in 66 countries were postponed. Childhood vaccination rates decreased across all WHO regions, with the Southeast Asian and eastern Mediterranean regions particularly affected. In India, more than 3 million children missed a first dose of the diphtheria, tetanus, and pertussis vaccine, more than double the number in 2019. “We [are] leaving children at risk from devastating but preventable diseases like measles, polio, or meningitis,” says WHO Director-General Tedros Adhanom Ghebreyesus. ### Climate policy As part of the run-up to the U.N. climate summit in November, the European Union and China announced last week plans to follow through on commitments to curb their carbon emissions. The European proposal, which must be approved by the bloc's member states, would steeply increase the price of carbon dioxide (CO2) emissions; eliminate new gas-powered cars by 2035; require 38% of all energy to come from renewables by 2030, up from a previous goal of 32%; and impose tariffs on goods from countries that have not acted on climate change. (Democratic lawmakers in the United States proposed a similar tariff this week.) Meanwhile, China on 16 July launched a carbon trading scheme for power plants that instantly created the world's largest carbon market, triple the European Union's in size. China's plan incentivizes plants to lower CO2 emissions by allowing more efficient facilities to sell some of their reductions to less efficient ones. Although some observers call the plan weak because it covers a relatively small portion of China's emissions, it could be expanded to eventually incorporate three-fourths of the country's emissions from all sources. ### Public health When temperatures soar, workers and their employers need to take heed: Hot weather led to 20,000 more injuries annually in California between 2001 and 2018, according to a novel analysis of 11 million workers compensation claims. Economist Jisung Park at the University of California, Los Angeles, and colleagues classified work-related injuries by ZIP code and looked up local temperatures on the day each was recorded. They found increases of between 5% and 15% in claims, depending on the temperature and occupation, compared with those filed on a typical cooler day, defined as a temperature of 16°C. Few were attributed directly to heat, but the injuries connected to higher temperatures—such as falls and mishandling equipment—may have resulted because the heat made workers woozy, the researchers reported to Congress last week and in a preprint on the SSRN server. But mitigation may be possible: Heat-related injury claims declined after 2005, when California began to require shade, water, and breaks for outdoor workers—in industries such as construction, utilities, and farming—whenever temperature exceeded 35°C. ### Research integrity Both the United Kingdom and the United States last week announced new high-level bodies to provide guidance on research integrity—but both lack the powers that many whistleblowers say are critical, such as independently investigating complaints of wrongdoing and pulling grant funding from institutions that fail to conduct misconduct probes properly. The umbrella funding body UK Research and Innovation (UKRI) launched the Committee on Research Integrity, which plans to operate for 3 years and accelerate existing projects in this area. The U.S. National Academies of Sciences, Engineering, and Medicine (NASEM) unveiled the Strategic Council for Research Excellence, Integrity, and Trust, which will have members from the U.S. National Institutes of Health and National Science Foundation. Unlike UKRI, NASEM does not fund researchers, so it cannot set policies on how to handle misconduct allegations. But it could promote integrity in other ways—for instance by pushing for a central repository for researchers to report their financial conflicts of interest, says Marcia McNutt, president of the National Academy of Sciences and an ex officio member of the new panel. ### Microbiology Sifting through DNA in the mud of her backyard, a geomicrobiologist discovered what may be the longest known extrachromosomal sequence, which includes genes from a variety of microbes—prompting her son to propose naming it after Star Trek 's Borg, cybernetic aliens that assimilate humans. Jill Banfield of the University of California, Berkeley, was searching for viruses that infect archaea, a type of microbe often found in places devoid of oxygen. The 1-million-base-pair strand of DNA contains genes known to help archaea metabolize methane, suggesting the fragment might exist inside the microbes but outside their normal chromosome, the research team wrote in a preprint posted on 10 July on the bioRxiv server. Scanning a public microbial DNA database, the authors identified 23 possible Borgs, with many of the same characteristics, in other U.S. locations. The Borgs' role remains murky, but they may provide another example of DNA that can hop between an organism's chromosomes or between organisms, helping species adapt to changes in their environment.


Proximity and single-molecule energetics

Science

Probing single molecules in their nanoenvironment can reveal site-specific phenomena that would be obscured by ensemble-averaging experiments on macroscopic populations of molecules. Particularly in the past decade, major technological breakthroughs in scanning probe microscopy (SPM) have led to unprecedented spatial resolution and versatility and enabled the interrogation of molecular conformation, bond order, molecular orbitals, charge states, spins, phonons, and intermolecular interactions. On page 452 of this issue, Peng et al. ([ 1 ][1]) use SPM to directly measure the triplet lifetime of an individual pentacene molecule and demonstrate its dependence on interactions with nearby oxygen molecules with atomic precision. In addition to allowing the local tuning and probing of spin-spin interactions between molecules, this study represents a notable advance in the single-molecule regime and provides insights into many macroscopic behaviors and related applications in catalysis, energy-conversion materials, or biological systems. Single-molecule studies have benefited from the high resolution achieved with well-defined functionalized probes, especially with carbon monoxide–terminated atomic force microscopy (AFM) tips ([ 2 ][2]). The versatility and applicability of AFM have also been enhanced by biasing the tip with gate voltages and supporting molecules on insulating substrates. In this configuration, the conductive AFM tip serves as an atomically controlled charge injector with single-charge sensitivity. Such electrical addressing of electronic states of single molecules ([ 3 ][3]) allows for the study of charge distribution and transport in single-molecule devices, organic electronics, and photovoltaics. Beyond steady-state spectroscopy, excited-state dynamics of single molecules can be measured by using an ultrashort and high-intensity electric (voltage) or optical (laser) pulse (the “pump”) to excite the sample. After a nonequilibrium state is generated, a second weaker pulse (the “probe”) monitors the change of the excited state. By varying the time delay between the two pulses, the temporal evolution of the excited state can be mapped out. Peng et al. used the electronic pump-probe approach in AFM to measure the lifetime of the excited triplet state of an individual pentacene molecule with atomic precision (see the figure). They observed strong quenching of the triplet lifetime by co-adsorbed molecular oxygen (O2). The electronic energy-transfer processes had an intriguing dependence on the arrangement of surrounding O2 molecules, which they controlled by atomic manipulation with the tip. Spin-relaxation measurements of single molecules in space with atomic resolution provide insights into their local interactions with each other, as well as with their nanoenvironment. Such information could be useful for spin-based quantum-information storage or quantum computing ([ 4 ][4]). Given the radiative relaxation of excited states, SPM-coupled optical spectroscopy provides a powerful tool to perform spatially and energy-resolved spectroscopic studies of single molecules. Specifically, site-resolved excitations of molecules can be induced by highly localized scanning tunnel microscopy (STM) current, and the resulting luminescence, which carries information that describes excited states, can be probed by integrated optical detection systems. This approach revealed redox state–dependent excitation of single molecules and intermolecular excitonic coupling interactions with atomic-scale spatial precision ([ 5 ][5], [ 6 ][6]). A study of electroluminescence demonstrated selective triplet formation by manipulating electron spin inside a molecule ([ 7 ][7]), which could provide a route to interrogate quantum spintronics and organic electronics at the single-molecule level. Besides tunneling electrons, the interaction of photons with molecules can provide valuable structural information and chemical identification through measurements of absorption, emission, or scattering of light. In particular, by confining laser light at the atomic-scale SPM junction and taking advantage of plasmon-enhanced Raman scattering, tip-enhanced Raman spectroscopy can overcome the diffraction limit of conventional optical spectroscopy and thereby achieve submolecular chemical spatial resolution ([ 8 ][8]). Such capability provides in-depth insights into single-molecule chemistry and site-specific chemical effects at the spatial limit ([ 9 ][9]). ![Figure][10] Atomically addressing excited single molecules The effect of nearby oxygen molecules on the lifetimes (τ) of triplet states T x , T y , and T z or T1 decaying to the singlet state S of individual pentacene molecules has been probed on an insulating salt surface. GRAPHIC: V. ALTOUNIAN/ SCIENCE Most excited states induced by photon absorption are incredibly short-lived (on the order of picoseconds to femtoseconds), so time-resolved optical STM techniques have been developed with ultrafast lasers. For example, pump-probe terahertz laser pulses were used to induce state-selective ultrafast STM tunneling currents through a single molecule. This approach allowed the molecular orbital structure and vibrations to be measured directly on the femtosecond time scale ([ 10 ][11]). Optical STM further showed the capability to explore photon and field-driven tunneling with angstrom-scale spatial resolution and attosecond temporal resolution. This experimental platform can be used to study quasiparticle dynamics in superconductor and two-dimensional materials with exceptional resolutions ([ 11 ][12]). Single-molecule studies could open avenues to access extremely transient states and chemical heterogeneity, suc h as the vibration of atoms within a molecule, the precession of a spin, ultrashort-lived complex reaction intermediates, and some key stochastic processes of reactions in chemistry and biology. For example, the study of Peng et al. relates to the reactivity of electronic excited states of organic molecules to O2 (and thus air). These processes can affect various natural photochemical and photophysical processes undergoing excitation by sunligh that can lead to transformation, degradation, or aging ([ 12 ][13]). The insightful descriptions of molecular conformation, dynamics, and function provided by spatially resolved single-molecule studies could inform complex and emergent behaviors of populations of molecules or even cells. 1. [↵][14]1. J. Peng et al ., Science 373, 452 (2021). [OpenUrl][15][Abstract/FREE Full Text][16] 2. [↵][17]1. L. Gross, 2. F. Mohn, 3. N. Moll, 4. P. Liljeroth, 5. G. Meyer , Science 325, 1110 (2009). [OpenUrl][18][Abstract/FREE Full Text][19] 3. [↵][20]1. S. Fatayer et al ., Nat. Nanotechnol. 13, 376 (2018). [OpenUrl][21][CrossRef][22][PubMed][23] 4. [↵][24]1. M. N. Leuenberger, 2. D. Loss , Nature 410, 789 (2001). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. Y. Zhang et al ., Nature 531, 623 (2016). [OpenUrl][29][CrossRef][30][PubMed][31] 6. [↵][32]1. B. Doppagne et al ., Science 361, 251 (2018). [OpenUrl][33][Abstract/FREE Full Text][34] 7. [↵][35]1. K. Kimura et al ., Nature 570, 210 (2019). [OpenUrl][36][CrossRef][37][PubMed][38] 8. [↵][39]1. J. Lee, 2. K. T. Crampton, 3. N. Tallarida, 4. V. A. Apkarian , Nature 568, 78 (2019). [OpenUrl][40][CrossRef][41][PubMed][42] 9. [↵][43]1. S. Mahapatra, 2. L. Li, 3. J. F. Schultz, 4. N. Jiang , J. Chem. Phys. 153, 010902 (2020). [OpenUrl][44] 10. [↵][45]1. T. L. Cocker, 2. D. Peller, 3. P. Yu, 4. J. Repp, 5. R. Huber , Nature 539, 263 (2016). [OpenUrl][46][CrossRef][47][PubMed][48] 11. [↵][49]1. M. Garg, 2. K. Kern , Science 367, 411 (2020). [OpenUrl][50][Abstract/FREE Full Text][51] 12. [↵][52]1. P. R. Ogilby , Chem. Soc. Rev. 39, 3181 (2010). [OpenUrl][53][CrossRef][54][PubMed][55] Acknowledgments: We acknowledge support from the National Science Foundation (CHE-1944796). 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Brain signals 'speak for person with paralysis

Science

A man unable to speak after a stroke has produced sentences through a system that reads electrical signals from speech production areas of his brain, researchers report this week. The approach has previously been used in nondisabled volunteers to reconstruct spoken or imagined sentences. But this first demonstration in a person who is paralyzed “tackles really the main issue that was left to be tackled—bringing this to the patients that really need it,” says Christian Herff, a computer scientist at Maastricht University who was not involved in the new work. The participant had a stroke more than a decade ago that left him with anarthria—an inability to control the muscles involved in speech. Because his limbs are also paralyzed, he communicates by selecting letters on a screen using small movements of his head, producing roughly five words per minute. To enable faster, more natural communication, neurosurgeon Edward Chang of the University of California, San Francisco, tested an approach that uses a computational model known as a deep-learning algorithm to interpret patterns of brain activity in the sensorimotor cortex, a brain region involved in producing speech ( Science , 4 January 2019, p. [14][1]). The approach has so far been tested in volunteers who have electrodes surgically implanted for nonresearch reasons such as to monitor epileptic seizures. In the new study, Chang's team temporarily removed a portion of the participant's skull and laid a thin sheet of electrodes smaller than a credit card directly over his sensorimotor cortex. To “train” a computer algorithm to associate brain activity patterns with the onset of speech and with particular words, the team needed reliable information about what the man intended to say and when. So the researchers repeatedly presented one of 50 words on a screen and asked the man to attempt to say it on cue. Once the algorithm was trained with data from the individual word task, the man tried to read sentences built from the same set of 50 words, such as “Bring my glasses, please.” To improve the algorithm's guesses, the researchers added a processing component called a natural language model, which uses common word sequences to predict the likely next word in a sentence. With that approach, the system only got about 25% of the words in a sentence wrong, they report this week in The New England Journal of Medicine . That's “pretty impressive,” says Stephanie Riès-Cornou, a neuroscientist at San Diego State University. (The error rate for chance performance would be 92%.) Because the brain reorganizes over time, it wasn't clear that speech production areas would give interpretable signals after more than 10 years of anarthria, notes Anne-Lise Giraud, a neuroscientist at the University of Geneva. The signals' preservation “is surprising,” she says. And Herff says the team made a “gigantic” step by generating sentences as the man was attempting to speak rather than from previously recorded brain data, as most studies have done. With the new approach, the man could produce sentences at a rate of up to 18 words per minute, Chang says. That's roughly comparable to the speed achieved with another brain-computer interface, described in Nature in May. That system decoded individual letters from activity in a brain area responsible for planning hand movements while a person who was paralyzed imagined handwriting. These speeds are still far from the 120 to 180 words per minute typical of conversational English, Riès-Cornou notes, but they far exceed what the participant can achieve with his head-controlled device. The system isn't ready for use in everyday life, Chang notes. Future improvements will include expanding its repertoire of words and making it wireless, so the user isn't tethered to a computer roughly the size of a minifridge. [1]: http://www.sciencemag.org/content/363/6422/14


Protein structure prediction now easier, faster

Science

Proteins are the minions of life, working alone or together to build, manage, fuel, protect, and eventually destroy cells. To function, these long chains of amino acids twist and fold and intertwine into complex shapes that can be slow, even impossible, to decipher. Scientists have dreamed of simply predicting a protein's shape from its amino acid sequence—an ability that would open a world of insights into the workings of life. “This problem has been around for 50 years; lots of people have broken their head on it,” says John Moult, a structural biologist at the University of Maryland, Shady Grove. But a practical solution is in their grasp. Several months ago, in a result hailed as a turning point, computational biologists showed that artificial intelligence (AI) could accurately predict protein shapes. Now, David Baker and Minkyung Baek at the University of Washington, Seattle, and their colleagues have made AI-based structure prediction more powerful and accessible. Their method, described online in Science this week, works on not just simple proteins, but also complexes of proteins, and its creators have made their computer code freely available. Since the method was posted online last month, the team has used it to model more than 4500 protein sequences submitted by other researchers. Savvas Savvides, a structural biologist at Ghent University, had tried six times to model a problematic protein. He says Baker's and Baek's program, called RoseTTAFold, “paved the way to a structure solution.” In fall of 2020, DeepMind, a U.K.-based AI company owned by Google, wowed the field with its structure predictions in a biennial competition ( Science , 4 December 2020, p. [1144][1]). Called Critical Assessment of Protein Structure Prediction (CASP), the competition uses structures newly determined using laborious lab techniques such as x-ray crystallography as benchmarks. DeepMind's program, AlphaFold2, did “really extraordinary things [predicting] protein structures with atomic accuracy,” says Moult, who organizes CASP. But for many structural biologists, AlphaFold2 was a tease: “Incredibly exciting but also very frustrating,” says David Agard, a structural biophysicist at the University of California, San Francisco. DeepMind has yet to publish its method and computer code for others to take advantage of. In mid-June, 3 days after the Baker lab posted its RoseTTAFold preprint, Demis Hassabis, DeepMind's CEO, tweeted that AlphaFold2's details were under review at a publication and the company would provide “broad free access to AlphaFold for the scientific community.” DeepMind's 30-minute presentation at CASP was enough to inspire Baek to develop her own approach. Like AlphaFold2, it uses AI's ability to discern patterns in vast databases of examples, generating ever more informed and accurate iterations as it learns. When given a new protein to model, RoseTTAFold proceeds along multiple “tracks.” One compares the protein's amino acid sequence with all similar sequences in protein databases. Another predicts pairwise interactions between amino acids within the protein, and a third compiles the putative 3D structure. The program bounces among the tracks to refine the model, using the output of each one to update the others. DeepMind's approach, although still under wraps, involves just two tracks, Baek and others believe. Gira Bhabha, a cell and structural biologist at New York University School of Medicine, says both methods work well. “Both the DeepMind and Baker lab advances are phenomenal and will change how we can use protein structure predictions to advance biology,” she says. A DeepMind spokesperson wrote in an email, “It's great to see examples such as this where the protein folding community is building on AlphaFold to work towards our shared goal of increasing our understanding of structural biology.” But AlphaFold2 solved the structures of only single proteins, whereas RoseTTAFold has also predicted complexes, such as the structure of the immune molecule interleukin-12 latched onto its receptor. Many biological functions depend on protein-protein interactions, says Torsten Schwede, a computational structural biologist at the University of Basel. “The ability to handle protein-protein complexes directly from sequence information makes it extremely attractive for many questions in biomedical research.” Baker concedes that, in general, AlphaFold2's structures are more accurate. But Savvides says the Baker lab's approach better captures “the essence and particularities of protein structure,” such as identifying strings of atoms sticking out of the sides of the protein—features key to interactions between proteins. Agard adds that Baker's and Baek's approach is faster and requires less computing power than DeepMind's, which relied on Google's massive servers. However, the DeepMind spokesperson wrote that its latest algorithm is more than 16 times as fast as the one it used at CASP in 2020. As a result, she wrote, “It's not clear to us that the system being described is an advance in speed.” Beginning on 1 June, Baker and Baek began to challenge their method by asking researchers to send in their most baffling protein sequences. Fifty-six head scratchers arrived in the first month, all of which have now predicted structures. Agard's group sent in an amino acid sequence with no known similar proteins. Within hours, his group got a protein model back “that probably saved us a year of work,” Agard says. Now, he and his team know where to mutate the protein to test ideas about how it functions. Because Baek's and Baker's group has released its computer code on the web, others can improve on it; the code has been downloaded 250 times since 1 July. “Many researchers will build their own structure prediction methods upon Baker's work,” says Jinbo Xu, a computational structural biologist at the Toyota Technological Institute at Chicago. Moult agrees: “When there's a breakthrough like this, 2 years later, everyone is doing it as well if not better than before.” [1]: http://www.sciencemag.org/content/370/6521/1144


Beware explanations from AI in health care

Science

Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions ([ 1 ][1]). However, despite the technical prowess of such systems, their adoption has been challenging, and whether and how much they will actually improve health care remains to be seen. A central reason for this is that the effectiveness of AI/ML-based medical devices depends largely on the behavioral characteristics of its users, who, for example, are often vulnerable to well-documented biases or algorithmic aversion ([ 2 ][2]). Many stakeholders increasingly identify the so-called black-box nature of predictive algorithms as the core source of users' skepticism, lack of trust, and slow uptake ([ 3 ][3], [ 4 ][4]). As a result, lawmakers have been moving in the direction of requiring the availability of explanations for black-box algorithmic decisions ([ 5 ][5]). Indeed, a near-consensus is emerging in favor of explainable AI/ML among academics, governments, and civil society groups. Many are drawn to this approach to harness the accuracy benefits of noninterpretable AI/ML such as deep learning or neural nets while also supporting transparency, trust, and adoption. We argue that this consensus, at least as applied to health care, both overstates the benefits and undercounts the drawbacks of requiring black-box algorithms to be explainable. It is important to first distinguish explainable from interpretable AI/ML. These are two very different types of algorithms with different ways of dealing with the problem of opacity—that AI predictions generated from a black box undermine trust, accountability, and uptake of AI. A typical AI/ML task requires constructing an algorithm that can take a vector of inputs (for example, pixel values of a medical image) and generate an output pertaining to, say, disease occurrence (for example, cancer diagnosis). The algorithm is trained on past data with known labels, which means that the parameters of a mathematical function that relate the inputs to the output are estimated from that data. When we refer to an algorithm as a “black box,” we mean that the estimated function relating inputs to outputs is not understandable at an ordinary human level (owing to, for example, the function relying on a large number of parameters, complex combinations of parameters, or nonlinear transformations of parameters). Interpretable AI/ML (which is not the subject of our main criticism) does roughly the following: Instead of using a black-box function, it uses a transparent (“white-box”) function that is in an easy-to-digest form, for example, a linear model whose parameters correspond to additive weights relating the input features and the output or a classification tree that creates an intuitive rule-based map of the decision space. Such algorithms have been described as intelligible ([ 6 ][6]) and decomposable ([ 7 ][7]). The interpretable algorithm may not be immediately understandable by everyone (even a regression requires a bit of background on linear relationships, for example, and can be misconstrued). However, the main selling point of interpretable AI/ML algorithms is that they are open, transparent, and capable of being understood with reasonable effort. Accordingly, some scholars argue that, under many conditions, only interpretable algorithms should be used, especially when they are used by governments for distributing burdens and benefits ([ 8 ][8]). However, requiring interpretability would create an important change to ML as it is being done today—essentially that we forgo deep learning altogether and whatever benefits it may entail. Explainable AI/ML is very different, even though both approaches are often grouped together. Explainable AI/ML, as the term is typically used, does roughly the following: Given a black-box model that is used to make predictions or diagnoses, a second explanatory algorithm finds an interpretable function that closely approximates the outputs of the black box. This second algorithm is trained by fitting the predictions of the black box and not the original data, and it is typically used to develop the post hoc explanations for the black-box outputs and not to make actual predictions because it is typically not as accurate as the black box. The explanation might, for instance, be given in terms of which attributes of the input data in the black-box algorithm matter most to a specific prediction, or it may offer an easy-to-understand linear model that gives similar outputs as the black-box algorithm for the same given inputs ([ 4 ][4]). Other models, such as so-called counterfactual explanations or heatmaps, are also possible ([ 9 ][9], [ 10 ][10]). In other words, explainable AI/ML ordinarily finds a white box that partially mimics the behavior of the black box, which is then used as an explanation of the black-box predictions. Three points are important to note: First, the opaque function of the black box remains the basis for the AI/ML decisions, because it is typically the most accurate one. Second, the white box approximation to the black box cannot be perfect, because if it were, there would be no difference between the two. It is also not focusing on accuracy but on fitting the black box, often only locally. Finally, the explanations provided are post hoc. This is unlike interpretable AI/ML, where the explanation is given using the exact same function that is responsible for generating the output and is known and fixed ex ante for all inputs. A substantial proportion of AI/ML-based medical devices that have so far been cleared or approved by the US Food and Drug Administration (FDA) use noninterpretable black-box models, such as deep learning ([ 1 ][1]). This may be because blackbox models are deemed to perform better in many health care applications, which are often of massively high dimensionality, such as image recognition or genetic prediction. Whatever the reason, to require an explanation of black-box AI/ML systems in health care at present entails using post hoc explainable AI/ML models, and this is what we caution against here. Explainable algorithms have been a relatively recent area of research, and much of the focus of tech companies and researchers has been on the development of the algorithms themselves—the engineering—and not on the human factors affecting the final outcomes. The prevailing argument for explainable AI/ML is that it facilitates user understanding, builds trust, and supports accountability ([ 3 ][3], [ 4 ][4]). Unfortunately, current explainable AI/ML algorithms are unlikely to achieve these goals—at least in health care—for several reasons. ### Ersatz understanding Explainable AI/ML (unlike interpretable AI/ML) offers post hoc algorithmically generated rationales of black-box predictions, which are not necessarily the actual reasons behind those predictions or related causally to them. Accordingly, the apparent advantage of explainability is a “fool's gold” because post hoc rationalizations of a black box are unlikely to contribute to our understanding of its inner workings. Instead, we are likely left with the false impression that we understand it better. We call the understanding that comes from post hoc rationalizations “ersatz understanding.” And unlike interpretable AI/ML where one can confirm the quality of explanations of the AI/ML outcomes ex ante, there is no such guarantee for explainable AI/ML. It is not possible to ensure ex ante that for any given input the explanations generated by explainable AI/ML algorithms will be understandable by the user of the associated output. By not providing understanding in the sense of opening up the black box, or revealing its inner workings, this approach does not guarantee to improve trust and allay any underlying moral, ethical, or legal concerns. There are some circumstances where the problem of ersatz understanding may not be an issue. For example, researchers may find it helpful to generate testable hypotheses through many different approximations to a black-box algorithm to advance research or improve an AI/ML system. But this is a very different situation from regulators requiring AI/ML-based medical devices to be explainable as a precondition of their marketing authorization. ### Lack of robustness For an explainable algorithm to be trusted, it needs to exhibit some robustness. By this, we mean that the explainability algorithm should ordinarily generate similar explanations for similar inputs. However, for a very small change in input (for example, in a few pixels of an image), an approximating explainable AI/ML algorithm might produce very different and possibly competing explanations, with such differences not being necessarily justifiable or understood even by experts. A doctor using such an AI/ML-based medical device would naturally question that algorithm. ### Tenuous connection to accountability It is often argued that explainable AI/ML supports algorithmic accountability. If the system makes a mistake, the thought goes, it will be easier to retrace our steps and delineate what led to the mistake and who is responsible. Although this is generally true of interpretable AI/ML systems, which are transparent by design, it is not true of explainable AI/ML systems because the explanations are post hoc rationales, which only imperfectly approximate the actual function that drove the decision. In this sense, explainable AI/ML systems can serve to obfuscate our investigation into a mistake rather than help us to understand its source. The relationship between explainability and accountability is further attenuated by the fact that modern AI/ML systems rely on multiple components, each of which may be a black box in and of itself, thereby requiring a fact finder or investigator to identify, and then combine, a sequence of partial post hoc explanations. Thus, linking explainability to accountability may prove to be a red herring. Explainable AI/ML systems not only are unlikely to produce the benefits usually touted of them but also come with additional costs (as compared with interpretable systems or with using black-box models alone without attempting to rationalize their outputs). ### Misleading in the hands of imperfect users Even when explanations seem credible, or nearly so, when combined with prior beliefs of imperfectly rational users, they may still drive the users further away from a real understanding of the model. For example, the average user is vulnerable to narrative fallacies, where users combine and reframe explanations in misleading ways. The long history of medical reversals—the discovery that a medical practice did not work all along, either failing to achieve its intended goal or carrying harms that outweighed the benefits—provides examples of the risks of narrative fallacy in health care. Relatedly, explanations in the form of deceptively simple post hoc rationales can engender a false sense of (over)confidence. This can be further exacerbated through users' inability to reason with probabilistic predictions, which AI/ML systems often provide ([ 11 ][11]), or the users' undue deference to automated processes ([ 2 ][2]). All of this is made more challenging because explanations have multiple audiences, and it would be difficult to generate explanations that are helpful for all of them. ### Underperforming in at least some tasks If regulators decide that the only algorithms that can be marketed are those whose predictions can be explained with reasonable fidelity, they thereby limit the system's developers to a certain subset of AI/ML algorithms. For example, highly nonlinear models that are harder to approximate in a sufficiently large region of the data space may thus be prohibited under such a regime. This will be fine in cases where complex models—like deep learning or ensemble methods—do not particularly outperform their simpler counterparts (characterized by fairly structured data and meaningful features, such as predictions based on relatively few patient medical records) ([ 8 ][8]). But in others, especially in cases with massively high dimensionality—such as image recognition or genetic sequence analysis—limiting oneself to algorithms that can be explained sufficiently well may unduly limit model complexity and undermine accuracy. If explainability should not be a strict requirement for AI/ML in health care, what then? Regulators like the FDA should focus on those aspects of the AI/ML system that directly bear on its safety and effectiveness—in particular, how does it perform in the hands of its intended users? To accomplish this, regulators should place more emphasis on well-designed clinical trials, at least for some higher-risk devices, and less on whether the AI/ML system can be explained ([ 12 ][12]). So far, most AI/ML-based medical devices have been cleared by the FDA through the 510(k) pathway, requiring only that substantial equivalence to a legally marketed (predicate) device be demonstrated, without usually requiring any clinical trials ([ 13 ][13]). Another approach is to provide individuals added flexibility when they interact with a model—for example, by allowing them to request AI/ML outputs for variations of inputs or with additional data. This encourages buy-in from the users and reinforces the model's robustness, which we think is more intimately tied to building trust. This is a different approach to providing insight into a model's inner workings. Such interactive processes are not new in health care, and their design may depend on the specific application. One example of such a process is the use of computer decision aids for shared decision-making for antenatal counseling at the limits of gestational viability. A neonatologist and the prospective parents might use the decision aid together in such a way to show how various uncertainties will affect the “risk:benefit ratios of resuscitating an infant at the limits of viability” ([ 14 ][14]). This reflects a phenomenon for which there is growing evidence—that allowing individuals to interact with an algorithm reduces “algorithmic aversion” and makes them more willing to accept the algorithm's predictions ([ 2 ][2]). ### From health care to other settings Our argument is targeted particularly to the case of health care. This is partly because health care applications tend to rely on massively high-dimensional predictive algorithms where loss of accuracy is particularly likely if one insists on the ability of good black-box approximations with simple enough explanations, and expertise levels vary. Moreover, the costs of misclassifications and potential harm to patients are relatively higher in health care compared with many other sectors. Finally, health care traditionally has multiple ways of demonstrating the reliability of a product or process, even in the absence of explanations. This is true of many FDA-approved drugs. We might think of medical AI/ML as more like a credence good, where the epistemic warrant for its use is trust in someone else rather than an understanding of how it works. For example, many physicians may be quite ignorant of the underlying clinical trial design or results that led the FDA to believe that a certain prescription drug was safe and effective, but their knowledge that it has been FDA-approved and that other experts further scrutinize it and use it supplies the necessary epistemic warrant for trusting the drug. But insofar as other domains share some of these features, our argument may apply more broadly and hold some lessons for regulators outside health care as well. ### When interpretable AI/ML is necessary Health care is a vast domain. Many AI/ML predictions are made to support diagnosis or treatment. For example, Biofourmis's RhythmAnalytics is a deep neural network architecture trained on electrocardiograms to predict more than 15 types of cardiac arrhythmias ([ 15 ][15]). In cases like this, accuracy matters a lot, and understanding is less important when a black box achieves higher accuracy than a white box. Other medical applications, however, are different. For example, imagine an AI/ML system that uses predictions about the extent of a patient's kidney damage to determine who will be eligible for a limited number of dialysis machines. In cases like this, when there are overarching concerns of justice— that is, concerns about how we should fairly allocate resources—ex ante transparency about how the decisions are made can be particularly important or required by regulators. In such cases, the best standard would be to simply use interpretable AI/ML from the outset, with clear predetermined procedures and reasons for how decisions are taken. In such contexts, even if interpretable AI/ML is less accurate, we may prefer to trade off some accuracy, the price we pay for procedural fairness. We argue that the current enthusiasm for explainability in health care is likely overstated: Its benefits are not what they appear, and its drawbacks are worth highlighting. For health AI/ML-based medical devices at least, it may be preferable not to treat explainability as a hard and fast requirement but to focus on their safety and effectiveness. Health care professionals should be wary of explanations that are provided to them for black-box AI/ML models. Health care professionals should strive to better understand AI/ML systems to the extent possible and educate themselves about how AI/ML is transforming the health care landscape, but requiring explainable AI/ML seldom contributes to that end. 1. [↵][16]1. S. Benjamens, 2. P. Dhunnoo, 3. B. Meskó , NPJ Digit. Med. 3, 118 (2020). [OpenUrl][17][PubMed][18] 2. [↵][19]1. B. J. Dietvorst, 2. J. P. Simmons, 3. C. Massey , Manage. Sci. 64, 1155 (2018). [OpenUrl][20] 3. [↵][21]1. A. F. Markus, 2. J. A. Kors, 3. P. R. Rijnbeek , J. Biomed. Inform. 113, 103655 (2021). [OpenUrl][22][PubMed][18] 4. [↵][23]1. M. T. Ribeiro, 2. S. Singh, 3. C. Guestrin , in KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2016), pp. 1135–1144. 5. [↵][24]1. A. Bohr, 2. K. Memarzadeh 1. S. Gerke, 2. T. Minssen, 3. I. G. Cohen , in Artificial Intelligence in Healthcare, A. Bohr, K. Memarzadeh, Eds. (Elsevier, 2020), pp. 295–336. 6. [↵][25]1. Y. Lou, 2. R. Caruana, 3. J. Gehrke , in KDD '12: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2012), pp. 150–158. 7. [↵][26]1. Z. C. Lipton , ACM Queue 16, 1 (2018). [OpenUrl][27] 8. [↵][28]1. C. Rudin , Nat. Mach. Intell. 1, 206 (2019). [OpenUrl][29] 9. [↵][30]1. D. Martens, 2. F. Provost , Manage. Inf. Syst. Q. 38, 73 (2014). [OpenUrl][31] 10. [↵][32]1. S. Wachter, 2. B. Mittelstadt, 3. C. Russell , Harv. J. Law Technol. 31, 841 (2018). [OpenUrl][33] 11. [↵][34]1. R. M. Hamm, 2. S. L. Smith , J. Fam. Pract. 47, 44 (1998). [OpenUrl][35][PubMed][36] 12. [↵][37]1. S. Gerke, 2. B. Babic, 3. T. Evgeniou, 4. I. G. Cohen , NPJ Digit. Med. 3, 53 (2020). [OpenUrl][38] 13. [↵][39]1. U. J. Muehlematter, 2. P. Daniore, 3. K. N. Vokinger , Lancet Digit. Health 3, e195 (2021). [OpenUrl][40] 14. [↵][41]1. U. Guillen, 2. H. Kirpalani , Semin. Fetal Neonatal Med. 23, 25 (2018). [OpenUrl][42][PubMed][18] 15. [↵][43]Biofourmis, RhythmAnalytics (2020); [www.biofourmis.com/solutions/][44]. Acknowledgments: We thank S. Wachter for feedback on an earlier version of this manuscript. All authors contributed equally to the analysis and drafting of the paper. Funding: S.G. and I.G.C. were supported by a grant from the Collaborative Research Program for Biomedical Innovation Law, a scientifically independent collaborative research program supported by a Novo Nordisk Foundation grant (NNF17SA0027784). I.G.C. was also supported by Diagnosing in the Home: The Ethical, Legal, and Regulatory Challenges and Opportunities of Digital Home Health, a grant from the Gordon and Betty Moore Foundation (grant agreement number 9974). Competing interests: S.G. is a member of the Advisory Group–Academic of the American Board of Artificial Intelligence in Medicine. I.G.C. serves as a bioethics consultant for Otsuka on their Abilify MyCite product. I.G.C. is a member of the Illumina ethics advisory board. I.G.C. serves as an ethics consultant for Dawnlight. The authors declare no other competing interests. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: #ref-15 [16]: #xref-ref-1-1 "View reference 1 in text" [17]: {openurl}?query=rft.jtitle%253DNPJ%2BDigit.%2BMed.%26rft.volume%253D3%26rft.spage%253D118%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [18]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F373%2F6552%2F284.atom [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DManage.%2BSci.%26rft.volume%253D64%26rft.spage%253D1155%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DJ.%2BBiomed.%2BInform.%26rft.volume%253D113%26rft.spage%253D103655%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: #xref-ref-4-1 "View reference 4 in text" [24]: #xref-ref-5-1 "View reference 5 in text" [25]: #xref-ref-6-1 "View reference 6 in text" [26]: #xref-ref-7-1 "View reference 7 in text" [27]: {openurl}?query=rft.jtitle%253DACM%2BQueue%26rft.volume%253D16%26rft.spage%253D1%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: #xref-ref-8-1 "View reference 8 in text" [29]: {openurl}?query=rft.jtitle%253DNat.%2BMach.%2BIntell.%26rft.volume%253D1%26rft.spage%253D206%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: #xref-ref-9-1 "View reference 9 in text" [31]: {openurl}?query=rft.jtitle%253DManage.%2BInf.%2BSyst.%2BQ.%26rft.volume%253D38%26rft.spage%253D73%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [32]: #xref-ref-10-1 "View reference 10 in text" [33]: {openurl}?query=rft.jtitle%253DHarv.%2BJ.%2BLaw%2BTechnol.%26rft.volume%253D31%26rft.spage%253D841%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: #xref-ref-11-1 "View reference 11 in text" [35]: {openurl}?query=rft.jtitle%253DThe%2BJournal%2Bof%2Bfamily%2Bpractice%26rft.stitle%253DJ%2BFam%2BPract%26rft.aulast%253DHamm%26rft.auinit1%253DR.%2BM.%26rft.volume%253D47%26rft.issue%253D1%26rft.spage%253D44%26rft.epage%253D52%26rft.atitle%253DThe%2Baccuracy%2Bof%2Bpatients%2527%2Bjudgments%2Bof%2Bdisease%2Bprobability%2Band%2Btest%2Bsensitivity%2Band%2Bspecificity.%26rft_id%253Dinfo%253Apmid%252F9673608%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [36]: /lookup/external-ref?access_num=9673608&link_type=MED&atom=%2Fsci%2F373%2F6552%2F284.atom [37]: #xref-ref-12-1 "View reference 12 in text" [38]: {openurl}?query=rft.jtitle%253DNPJ%2BDigit.%2BMed.%26rft.volume%253D3%26rft.spage%253D53%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: #xref-ref-13-1 "View reference 13 in text" [40]: {openurl}?query=rft.jtitle%253DLancet%2BDigit.%2BHealth%26rft.volume%253D3%26rft.spage%253D195e%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: #xref-ref-14-1 "View reference 14 in text" [42]: {openurl}?query=rft.jtitle%253DSemin.%2BFetal%2BNeonatal%2BMed.%26rft.volume%253D23%26rft.spage%253D25%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: #xref-ref-15-1 "View reference 15 in text" [44]: http://www.biofourmis.com/solutions/


Estimating epidemiologic dynamics from cross-sectional viral load distributions

Science

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, polymerase chain reaction (PCR) tests were generally reported only as binary positive or negative outcomes. However, these test results contain a great deal more information than that. As viral load declines exponentially, the PCR cycle threshold (Ct) increases linearly. Hay et al. developed an approach for extracting epidemiological information out of the Ct values obtained from PCR tests used in surveillance for a variety of settings (see the Perspective by Lopman and McQuade). Although there are challenges to relying on single Ct values for individual-level decision-making, even a limited aggregation of data from a population can inform on the trajectory of the pandemic. Therefore, across a population, an increase in aggregated Ct values indicates that a decline in cases is occurring. Science , abh0635, this issue p. [eabh0635][1]; see also abj4185, p. [280][2] ### INTRODUCTION Current approaches to epidemic monitoring rely on case counts, test positivity rates, and reported deaths or hospitalizations. These metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points. ### RATIONALE We develop a new method that uses information inherent in cycle threshold (Ct) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests to robustly estimate the epidemic trajectory from multiple or even a single cross section of positive samples. Ct values are related to viral loads, which depend on the time since infection; Ct values are generally lower when the time between infection and sample collection is short. Despite variation across individuals, samples, and testing platforms, Ct values provide a probabilistic measure of time since infection. We find that the distribution of Ct values across positive specimens at a single time point reflects the epidemic trajectory: A growing epidemic will necessarily have a high proportion of recently infected individuals with high viral loads, whereas a declining epidemic will have more individuals with older infections and thus lower viral loads. Because of these changing proportions, the epidemic trajectory or growth rate should be inferable from the distribution of Ct values collected in a single cross section, and multiple successive cross sections should enable identification of the longer-term incidence curve. Moreover, understanding the relationship between sample viral loads and epidemic dynamics provides additional insights into why viral loads from surveillance testing may appear higher for emerging viruses or variants and lower for outbreaks that are slowing, even absent changes in individual-level viral kinetics. ### RESULTS Using a mathematical model for population-level viral load distributions calibrated to known features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load kinetics, we show that the median and skewness of Ct values in a random sample change over the course of an epidemic. By formalizing this relationship, we demonstrate that Ct values from a single random cross section of virologic testing can estimate the time-varying reproductive number of the virus in a population, which we validate using data collected from comprehensive SARS-CoV-2 testing in long-term care facilities. Using a more flexible approach to modeling infection incidence, we also develop a method that can reliably estimate the epidemic trajectory in even more-complex populations, where interventions may be implemented and relaxed over time. This method performed well in estimating the epidemic trajectory in the state of Massachusetts using routine hospital admissions RT-qPCR testing data—accurately replicating estimates from other sources for the entire state. ### CONCLUSION This work provides a new method for estimating the epidemic growth rate and a framework for robust epidemic monitoring using RT-qPCR Ct values that are often simply discarded. By deploying single or repeated (but small) random surveillance samples and making the best use of the semiquantitative testing data, we can estimate epidemic trajectories in real time and avoid biases arising from nonrandom samples or changes in testing practices over time. Understanding the relationship between population-level viral loads and the state of an epidemic reveals important implications and opportunities for interpreting virologic surveillance data. It also highlights the need for such surveillance, as these results show how to use it most informatively. ![Figure][3] Ct values reflect the epidemic trajectory and can be used to estimate incidence. ( A and B ) Whether an epidemic has rising or falling incidence will be reflected in the distribution of times since infection (A), which in turn affects the distribution of Ct values in a surveillance sample (B). ( C ) These values can be used to assess whether the epidemic is rising or falling and estimate the incidence curve. Estimating an epidemic’s trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance—in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing—changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic’s trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response. [1]: /lookup/doi/10.1126/science.abh0635 [2]: /lookup/doi/10.1126/science.abj4185 [3]: pending:yes


Senolytics reduce coronavirus-related mortality in old mice

Science

Cellular senescence is a state elicited in response to stress signals and is associated with a damaging secretory phenotype. The number of senescent cells increases with advanced age and this in turn drives age-related diseases. Camell et al. show that senescent cells have an amplified inflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (see the Perspective by Cox and Lord). This response is communicated to nonsenescent cells, suppressing viral defense mechanisms and increasing the expression of viral entry proteins. In old mice infected with a SARS-CoV-2–related virus, treatment with senolytics to reduce the senolytic cell burden reduced mortality and increased antiviral antibodies. Science , abe4832, this issue p. [eabe4832][1]; see also abi4474, p. [281][2] ### INTRODUCTION The COVID-19 pandemic revealed enhanced vulnerability of the elderly and chronically ill to adverse outcomes upon severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Senescence is a cell fate elicited by cellular stress that results in changes in gene expression, morphology, metabolism, and resistance to apoptosis. Senescent cells (SnCs) secrete pro-inflammatory factors, called the senescence-associated secretory phenotype (SASP). SnCs accumulate with age and drive chronic inflammation. In human cells and tissues and using a new infection paradigm, we asked whether SnCs are a cause of adverse outcomes of infection with aging. This is relevant because SnCs can be selectively eliminated in vivo with a new class of therapeutics called senolytics, potentially affording a new approach to treat COVID-19. ### RATIONALE We hypothesized that SnCs, because of their pro-inflammatory SASP, might have a heightened response to pathogen-associated molecular pattern (PAMP) factors, resulting in increased risk of cytokine storm and multi-organ failure. To test this, we treated senescent and nonsenescent human cells with the PAMPs lipopolysaccharide (LPS) and SARS-CoV-2 spike protein (S1) and measured the SASP and its effect on non-SnCs. Similarly, old and progeroid mice were challenged with LPS, and we measured the SASP. Previously, we created a “normal microbial experience” (NME) for mice by transmitting environmental pathogens to specified-pathogen–free (SPF) mice through exposure to pet store mice or their bedding. The first pathogen transferred was mouse hepatitis virus (MHV), a β-coronavirus closely related to SARS-CoV-2. NME rapidly killed aged SPF mice known to have an increased burden of SnCs compared with young SPF mice, which survive NME. This afforded an experimental paradigm to test whether senolytics blunt adverse outcomes in β-coronavirus infection. ### RESULTS Human endothelial SnCs became hyperinflammatory in response to challenge with LPS and S1, relative to non-SnCs. The PAMP-elicited secretome of SnCs caused increased expression of viral entry proteins and reduced expression of antiviral genes in nonsenescent human endothelial and lung epithelial cells, and the proximity of these events was established in human lung biopsies. Treatment of old mice with LPS significantly increased SASP expression in several organs relative to young mice, confirming our hypothesis in vivo. Similarly, old mice exposed to NME displayed a significant multi-organ increase in SnCs and the SASP, impaired immune response to MHV, and 100% mortality, whereas inoculation with antibodies against MHV before NME afforded complete rescue of mortality. Treating old mice with the senolytic fisetin, which selectively eliminates SnCs after NME reduced mortality by 50%, reduced expression of inflammatory proteins in serum and tissue and improved the immune response. This was confirmed with a second senolytic regimen, Dasatinib plus Quercetin, as well as genetic ablation of SnCs in aged mice, establishing SnCs as a cause of adverse outcomes in aged organisms exposed to a new viral pathogen. ### CONCLUSION SnCs amplify susceptibility to COVID-19 and pathogen-induced hyperinflammation. Reducing SnC burden in aged mice reduces mortality after pathogen exposure, including a β-coronavirus. Our findings strongly support the Geroscience hypothesis that therapeutically targeting fundamental aging mechanisms improves resilience in the elderly, with alleviation of morbidity and mortality due to pathogenic stress. This suggests that senolytics might protect others vulnerable to adverse COVID-19 outcomes in whom increased SnCs occur (such as in obesity or numerous chronic diseases). ![Figure][3] SnCs that accumulate with age or chronic disease react to PAMPs such as SARS-CoV-2 S1 by amplifying the SASP, which increases viral entry protein expression and decreases viral defense IFITMs in normal cells. Old mice exposed to pathogens such as the β-coronavirus MHV have increased inflammation and higher mortality. Treatment with a senolytic decreased SnCs, inflammation, and mortality and increased the antiviral antibody response. The COVID-19 pandemic has revealed the pronounced vulnerability of the elderly and chronically ill to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–induced morbidity and mortality. Cellular senescence contributes to inflammation, multiple chronic diseases, and age-related dysfunction, but effects on responses to viral infection are unclear. Here, we demonstrate that senescent cells (SnCs) become hyper-inflammatory in response to pathogen-associated molecular patterns (PAMPs), including SARS-CoV-2 spike protein-1, increasing expression of viral entry proteins and reducing antiviral gene expression in non-SnCs through a paracrine mechanism. Old mice acutely infected with pathogens that included a SARS-CoV-2–related mouse β-coronavirus experienced increased senescence and inflammation, with nearly 100% mortality. Targeting SnCs by using senolytic drugs before or after pathogen exposure significantly reduced mortality, cellular senescence, and inflammatory markers and increased antiviral antibodies. Thus, reducing the SnC burden in diseased or aged individuals should enhance resilience and reduce mortality after viral infection, including that of SARS-CoV-2. [1]: /lookup/doi/10.1126/science.abe4832 [2]: /lookup/doi/10.1126/science.abi4474 [3]: pending:yes


A sustainable use of space

Science

Last month, at the G7 Leaders' Summit in Cornwall, United Kingdom, the leading industrial nations addressed the sustainable and safe use of space, making space debris a priority and calling on other nations to follow suit. This is good news because space is becoming increasingly congested, and strong political will is needed for the international space community to start using space sustainably and preserve the orbital environment for the space activities of future generations. There are more than 28,000 routinely tracked objects orbiting Earth. The vast majority (85%) are space debris that no longer serve a purpose. These debris objects are dominated by fragments from the approximately 560 known breakups, explosions, and collisions of satellites or rocket bodies. These have left behind an estimated 900,000 objects larger than 1 cm and a staggering 130 million objects larger than 1 mm in commercially and scientifically valuable Earth orbits. Today's already active satellite infrastructure provides a multitude of critical services to modern society, including communication, weather, navigation, and Earth-monitoring missions. Its loss would severely damage modern society. Furthermore, a new era in space has just started, driven by commercial, low-latency broadband services that rely on large constellations of satellites in low Earth orbit. These will revolutionize connectivity on the ground and in the air. However, they will also increase space traffic. The satellites to be launched over the next 5 years will surpass the number launched globally over the entire history of spaceflight. Congestion in space is only going to get worse. It is apparent that debris mitigation strategies—defined two decades ago by experts in the world's leading space agencies—are ever more important. They aim to prevent explosive breakups by venting residual energy from space systems at the end of their missions, and to “dispose” of a space object through a final maneuver that causes it to reenter Earth's atmosphere. Although these strategies are widely recognized, dozens of large space objects are still stranded every year in critical orbital regions where they will remain for several hundred years. And an average of eight fragmentation events in orbit occur annually, adding more pollution and increasing the likelihood of more collisions. Operations in space are themselves facing the burden of increasing evasive maneuvers to prevent losing a mission. In the most densely populated orbital altitudes, space objects are receiving dozens of collision warnings per day, of which only the most critical can be avoided. The number of such alerts will grow as large constellations of satellites come online. Another important facet of the debris problem is the risk on Earth from reentering objects. Between 100 and 200 metric tons of human-made hardware reenters Earth's atmosphere every year in an uncontrolled fashion. Heat-resistant material, like titanium or stainless steel, can survive the harsh reentry conditions. Progress can be made by advancing technology to ensure spaceflight safety. For example, the European Space Agency's Space Safety Programme is developing solutions that make disposal and energy passivation actions more fail-safe. “Deorbiting kits” will provide redundant propulsion and communication to ensure disposal of a spacecraft even after it ceases to function. A new field of “design-to-demise” will aim to replace critical components with less heat-resistant material to limit their chance of reaching ground upon reentry. In addition, a more systematic deployment of ground-based laser tracking could increase the accuracy of space surveillance data and consequently limit the number of collision avoidance alerts. Laser power could even transfer a small amount of momentum to objects to prevent their collisions. On top of that, missions, such as Clearspace-1, will aim to remove targeted debris through robotic capture. An internationally binding regime for the management of debris and space traffic is pending. Thus far, space missions have been supervised on the national level only, and states have been encouraged to translate the nonbinding space debris guidelines into national regulations. Space, however, is a commonly used resource with a limited capacity. International harmonization of space traffic would be required for an efficient and interference-free use of space. The coordinated use of the available radio frequencies could serve as a template. Furthermore, the implementation of space debris mitigation requirements should be tracked, following internationally binding principles. New and affordable technical solutions might stimulate more ambitious steps in international regulation to preserve space for the spacefarers of tomorrow.


Quantifying host-microbiota interactions

Science

The human microbiota is a complex microbial community living on and in our bodies. Its impact on a host's health is immense, affecting digestion ([ 1 ][1]), the immune system ([ 2 ][2]), behavior ([ 3 ][3]), metabolic diseases ([ 4 ][4]), and responses to drugs ([ 5 ][5]–[ 7 ][6]). Rapid advances in experimental and computational methods have moved the human microbiome field from identifying associations between microbiota composition and host health to unraveling the underlying molecular mechanisms ([ 8 ][7]–[ 10 ][8]). However, exactly how much the microbiota contributes to host health is a very difficult question to answer. By focusing on mechanistic and quantitative questions about the microbiome's contributions to host metabolism, I leverage my background in applied mathematics and systems biology to develop computational models describing host-microbiota interactions. Good models require good data from controlled experiments—a challenging proposition in complex host-microbiota systems. As a postdoc, I joined Andy Goodman's lab at Yale University and found myself in a perfect position to collect such data. By combining bacterial genetics with gnotobiotic mouse models, I learned how to modify the microbiome of germ-free, sterile mice. In the Goodman lab, we used these mice to study the contribution of microbiota to host metabolism of a number of pharmaceutical drugs. We found that this was also a good system to quantify host-microbiome interactions in vivo, because the compounds we used can be introduced into the system in a controlled way. We first focused on brivudine, an antiviral compound that can be converted into a potentially toxic metabolite, bromovinyluracil (BVU), by either a host or its microbiome ([ 11 ][9]). To identify bacteria capable of converting brivudine to BVU, we incubated individual bacterial species with the drug in vitro. One of the most potent brivudine metabolizers was Bacteroides thetaiotaomicron , a common gut bacterium with a genetic deletion library readily available. By incubating this library with the drug, we identified one bacterial mutant that had lost the capacity to convert brivudine to BVU. We then colonized germ-free mice with either the wild-type or mutant B. thetaiotaomicron , which provided us with a controllable host-microbiome system and two mouse groups that were identical, save for a single bacterial gene. When we administered brivudine to these two groups, the observed outcome was somewhat puzzling. Although drug levels in the intestine were much higher in mice colonized with the mutant bacterium, serum levels were comparable between the two mouse groups. The metabolite levels showed the opposite pattern: no difference (and very low levels) in the intestine but much higher metabolite levels in the sera of mice colonized with the wild-type bacterium (see the figure). These data could potentially be explained by bacterial conversion of the drug in the intestine and the rapid metabolite absorption into the serum. To test this explanation, we started with a simple kinetic model with two equations describing host drug metabolism in the liver and bacterial drug metabolism in the intestine. Once solved, this equation system showed that the difference between the amounts of metabolite absorbed into the sera of each of the two mouse groups was determined by the amount of BVU produced by microbes in the gut. This controlled experimental setup allowed us to quantify that the bacterial contribution to the toxic drug metabolite in vivo was about 70% ([ 12 ][10]) (see the figure). We expanded the model to describe drug metabolism processes in eight different tissues and in enterohepatic circulation (when the drug metabolized in the liver is secreted back into the small intestine via bile). We then demonstrated that our approach can be generalized to estimate the bacterial contribution to drug metabolism even if the metabolizing species remain unknown by using data from germ-free mice and mice harboring a complex microbial community. We also showed that microbial contribution to the drug metabolite far exceeds the host for sorivudine, an antiviral drug with different host and microbiome metabolism rates, and for clonazepam, an anxiolytic and anticonvulsant drug converted to multiple metabolites ([ 12 ][10]). ![Figure][11] Experimental and computational approaches that quantify host and microbial contributions to drug metabolism Oral drugs are administered to gnotobiotic mice that differ in a single microbial drug-metabolizing enzyme (GNMUT, mutant; GNWT, wild type); drug and drug metabolite kinetics are then quantified across tissues. A microbiome-host pharmacokinetic model developed from these measurements accurately predicts serum metabolite exposure and untangles host and microbiome contributions to drug metabolism. GRAPHIC: ADAPTED FROM M. ZIMMERMANN-KOGADEEVA BY N. CARY/ SCIENCE Quantifying the metabolic host-microbiome interactions is not the only purpose of our model. Having a robust model of host-microbiome interaction allows us to study, explain, and predict the system's behavior in different conditions. By analyzing how drug and metabolite profiles change when model parameters are varied, we found that the similarity of drug serum profiles between germ-free and colonized mice can be explained by the fast and microbiota-independent drug absorption from the small intestine. Our model further suggests that even for rapidly absorbed drugs, microbiome contributions to a host's metabolism can be substantial under certain conditions (e.g., a high microbiome to host ratio of drug metabolism or extensive enterohepatic circulation of the drug and its metabolites) ([ 13 ][12]). Such computational models enable us to investigate host-microbiota interactions in silico, guide experimental design, and help reduce the number of experiments needed to confirm model predictions. To systematically investigate microbial capacity to metabolize drugs, we next conducted a high-throughput in vitro screen. We found that microbiota contribution to drug metabolism might even be more widespread than we anticipated—two-thirds (176 out of 271) of the human-targeted drugs we examined were metabolized by at least one of the 76 tested bacteria ([ 14 ][13]). Although follow-up studies are required to test these microbiota-drug interactions in vivo, our findings emphasize that the microbiota should be considered when developing new drugs, stratifying patients, and choosing the most efficient treatment strategies. In the future, I believe that computational models combined with quantitative experimental data will allow us to measure host-microbiome interactions beyond drug metabolism and to better understand, predict, and control the effect of the microbiome on our health in everyday life. FINALIST Maria Zimmermann-Kogadeeva Maria Zimmermann-Kogadeeva received undergraduate degrees from Lomonosov Moscow State University in Russia and a PhD from ETH Zürich, Switzerland. After completing her postdoctoral fellowships at Yale University in the Goodman group and at European Molecular Biology Laboratory (EMBL) Heidelberg in the Bork group, Maria will start her laboratory in the Genome Biology Unit at EMBL Heidelberg in 2021. 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