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New Research Suggests Artificial Brains Could Benefit From Sleep

#artificialintelligence

Researchers from Duke University have developed an AI model capable of taking highly blurry, pixellated images and rendering them with high detail. According to TechXplore, the model is capable of taking relatively few pixels and scaling the images up to create realistic looking faces that are approximately 64 times the resolution of the original image. The model hallucinates, or imagines, features that are between the lines of the original image. The research is an example of super-resolution. As Cynthia Rudin from Duke University's computer science team explained to TechXplore, this research project sets a record for super-resolution, as never before have images been created with such feal from such a small sample of starting pixels.


Unique material design for brain-like computations

#artificialintelligence

Researchers at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory say this may be changing as they endeavor to design computers inspired by the human brain's neural structure. As part of a collaboration with Lehigh University, Army researchers have identified a design strategy for the development of neuromorphic materials. "Neuromorphic materials is a name given to the material categories or combination of materials that provide both computing and memory capabilities in devices," said Dr. Sina Najmaei, a research scientist and electrical engineer with the laboratory. Najmaei and his colleagues published a paper, Dynamically reconfigurable electronic and phononic properties in intercalated Hafnium Disulfide (HfS2), in the May 2020 issue of Materials Today. The neuromorphic computing concept is an in-memory solution that promises orders of magnitude reductions in power consumption over conventional transistors, and is suitable for complex data classification and processing.


Does the Human Touch + AI = The Future of Work?

#artificialintelligence

Artificial intelligence has long caused fear of job loss across many sectors as companies look for ways to cut costs, support workers and become more profitable. But new research suggests that even in STEM-based sectors like cybersecurity, AI simply can't replace some traits found only in humans, such as creativity, intuition and experience. There's no doubt, AI certainly has its place. And most business leaders agree that AI is important to the future success of their company. A recent survey found CEOs believe the benefits of AI include creating better efficiencies (62 percent), helping businesses remain competitive (62 percent), and allowing organizations to gain a better understanding of their customers, according to Ernst and Young.


On Moving from Statistics to Machine Learning, the Final Stage of Grief

#artificialintelligence

I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.


How robots could help injured workers recover

#artificialintelligence

Training robots to guide injured workers through simulated tasks could make return-to-work evaluations and treatment programs more effective and accessible, according to researchers at the University of Alberta. In a review of scientific literature on efforts to use robotics for occupational rehabilitation, the researchers reported that robots with machine learning capabilities have the potential to accurately reproduce the physical activities workers experience and provide precise measurements of patients' abilities. "This research will hopefully improve the assessment of work ability and rehabilitation treatments, and lead to safer and more sustainable return-to-work efforts after injury," said Doug Gross, a physical therapist and professor in the Faculty of Rehabilitation Medicine. He noted the technology could be used during times of physical distancing, and expand rehabilitation services to people living in rural areas who would otherwise have to travel to access them. "You could have a therapist in Edmonton programming the robot and the patient in a rural area responding with what the therapist wants them to do," explained Gross.


AI Screens of Pandemic Job Seekers Could Lead to Bias Claims (1)

#artificialintelligence

Companies are making more use of algorithmic hiring tools to screen a flood of job applicants during the coronavirus pandemic amid questions about whether they introduce new forms of bias into the early vetting process. The tools are designed to more efficiently filter out candidates that don't meet certain job-related criteria, like prior work experience, and to recruit potential hires via their online profiles. Businesses like HireVue offer biometric scanning tools that give applicant feedback based on facial expressions, while others like Pymetrics use behavioral tests to home in on ideal candidates. Companies including Colgate-Palmolive Co., McDonald's Corp., Boston Consulting Group Inc., PricewaterhouseCoopers LLP, and Kraft Heinz Co. are using them at a time when 21 million people in the U.S. were without jobs and seeking employment in May, according to the Labor Department. Job candidates might be unable or unwilling to apply and interview in person because of rules limiting social gatherings, said Monica Snyder, a workplace privacy attorney at Fisher Phillips in Boston.


Research reflects how AI sees through the looking glass

#artificialintelligence

Right hands become left hands. Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell University researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards -- findings with implications for training machine learning models and detecting faked images. "The universe is not symmetrical. If you flip an image, there are differences," said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of the study, "Visual Chirality," presented at the 2020 Conference on Computer Vision and Pattern Recognition, held virtually June 14-19.


Artificial intelligence brings pancreatic cancer screening one step closer to reality

#artificialintelligence

Artificial intelligence (AI) holds promise for enabling earlier detection of pancreatic cancer, which is crucial to saving lives. The potential of AI is showcased in a study to be presented at the ESMO World Congress on Gastrointestinal Cancer, 1–4 July 2020. Overall, 12 in every 100,000 people develop pancreatic cancer. This means that screening everyone would be inefficient and would expose many people to unnecessary tests and potential side-effects. Between 70-80% of patients are diagnosed at an advanced stage when it is too late for curative treatment and five years after diagnosis, just 6% of patients have survived.


Improve alignment of research policy and societal values

Science

Historically, scientific and engineering expertise has been key in shaping research and innovation (R&I) policies, with benefits presumed to accrue to society more broadly over time ([ 1 ][1]). But there is persistent and growing concern about whether and how ethical and societal values are integrated into R&I policies and governance, as we confront public disbelief in science and political suspicion toward evidence-based policy-making ([ 2 ][2]). Erosion of such a social contract with science limits the ability of democratic societies to deal with challenges presented by new, disruptive technologies, such as synthetic biology, nanotechnology, genetic engineering, automation and robotics, and artificial intelligence. Many policy efforts have emerged in response to such concerns, one prominent example being Europe's Eighth Framework Programme, Horizon 2020 (H2020), whose focus on “Responsible Research and Innovation” (RRI) provides a case study for the translation of such normative perspectives into concrete policy action and implementation. Our analysis of this H2020 RRI approach suggests a lack of consistent integration of elements such as ethics, open access, open innovation, and public engagement. On the basis of our evaluation, we suggest possible pathways for strengthening efforts to deliver R&I policies that deepen mutually beneficial science and society relationships. Alignment of R&I objectives with societal benefits, which transcend exclusive economic value, is a globally relevant concern ([ 3 ][3]). Aspiration of stronger science and society interrelationships have been visible in U.S. research management efforts, as well as in Canada and Europe. In H2020, to which the European Commission (EC) allocated nearly €80 billion for the 2014–2020 funding period, the EC enumerated RRI as a priority across all of H2020 activities (a “cross-cutting issue”) to deepen science and society relationships and be responsive to societal challenges. To date, €1.88 billion have been invested across 200 different R&I areas (e.g., quantum computing, graphene nanotechnology, human brain research, artificial intelligence) in more than 1100 projects related to various dimensions of RRI (see the figure). Inclusion of RRI in H2020 reflected the commitment of the European Union (EU) to the precautionary principle with regard to R&I policy, and the deepening commitment of the EC to mainstream concerns related to science and society integration ([ 4 ][4], [ 5 ][5]). RRI principles and practices have been designed to enhance inclusive and democratic modes of conducting R&I to reflect current forms and aspirations of society ([ 4 ][4]). Formal adoption and exploitation of RRI in H2020 coalesced around six thematic domains of responsibility (“keys”): public engagement, gender equality, science education and science literacy, open access, ethics, and governance ([ 6 ][6]). As a relatively young concept, these six keys cover only a part of RRI as it is discussed in the academic literature. Their integration in the European R&I ecosystem was advanced by various political- and policy-level ambitions ([ 3 ][3]–[ 5 ][5]). The forthcoming Ninth Framework Programme, Horizon Europe (2021–2027), includes further mention of RRI, as well as additional efforts to increase responsiveness of science to society through elements of the so-called “three O's agenda” (i.e., open innovation, open science, openness to the world) ([ 7 ][7]). Despite this fairly extensive history of EC investment in mainstreaming activities, a recent survey of more than 3100 European researcher recipients of H2020 funding showed that a vast majority of respondents were not familiar with the concept of RRI ([ 8 ][8]). Although these findings by no means suggest that researchers are irresponsible, they raise questions about the success of the EC approach to embedding normative targets for responsibility into R&I. The need for systematic evaluation is clear ([ 9 ][9]). Our study contributes to a legacy of research on the efficacy of framework programmes in light of various EC ambitions ([ 10 ][10]). To answer our question about policy integration and implementation of RRI in H2020, we conducted a mixed method investigation in three stages: (i) desktop research, (ii) interviews, and (iii) case research [see supplementary materials (SM) S10 for details]. First, we collected and reviewed relevant documentation of the four H2020 Programme Sections (Excellent Science, Industrial Leadership, Societal Challenges, Diversity of Approaches) and 19 respective subthemes available on the websites of the EC. This included reviews of documents at the following levels: policy, scoping, work package, calls, projects, proposal templates, and evaluations. Review of documents extended to all three periods of H2020 (2014–2015, 2016–2017, and 2018–2020) and employed the six EC RRI keys as indicators. Second, we conducted interviews with representatives ( n = 257) of seven stakeholder groups within the 19 subthemes of H2020. Third, using natural language processing algorithms, we obtained and analyzed texts describing project objectives of all the H2020 projects (ongoing and finished, n = 13,644) available on the CORDIS Portal, which provides information on EU-funded R&I activities. We examined how proposal language and RRI policies translate into project activities across H2020 using text-mining approaches. We carried out keyword frequency analysis by applying a selection of 10 to 12 keywords (SM S8) associated with each of the six RRI keys. This resulted in an “RRI score” for each of six keys for each H2020 project (SM S13). This subsequent case research covered all three H2020 periods (i.e., 2014–2015, 2016–2017, and 2018–2020). At each of these stages we produced reports for each corresponding subtheme (SM S11). The resulting body of 19 reports was then systematically reviewed for levels of policy integration. The policy-integration levels were qualitatively assessed with the EC's own indicator assessment ([ 6 ][6]). ![Figure][11] How well is Responsible Research and Innovation represented in Horizon 2020? Limited high-quality reference to Responsible Research and Innovation (RRI) suggests that it has largely been referred to without proper understanding, or as an empty signifier. Data combine all four Horizon 2020 (H2020) program sections and reflect the amount and quality of representation of six RRI keys and three “O's,” across three levels: samples of internal H2020 program documents, H2020 stakeholder interviews, and H2020 project objectives. Comparison across keys within a given level is straightforward; all values are drawn from the same underlying materials. Comparison across levels within a given key should focus on relative proportions of the four colors within a given level, not on absolute values; analyses drew upon different types and amounts of underlying materials in each level. See supplementary materials for details. GRAPHIC: X. LIU/ SCIENCE This assessment demonstrates which elements of the RRI framework were initially defined by the policy-makers (desktop level), which RRI attributes the stakeholders were most aware of (interview level), and which RRI elements were manifested in project proposals (case level) (SM S12; see the figure). RRI as a concept has been present in most of the four Programme Sections of H2020, and particular RRI policy elements emerge as prominent in certain subthemes, especially those addressing societal challenges or explicitly promoting the uptake of RRI. But RRI overall has largely been referred to either without proper understanding of its definition, or as empty signifier, suggesting lack of compliance with the EC's interpretation of the RRI concept (see the figure; SM S9). Integration of the three O's agenda, contemplated as a successor to the RRI framework, lagged behind that of the six RRI keys; a finding consistent with introduction of the agenda in the later stages of H2020. Our results suggest that the integration of the RRI framework into H2020 has fallen short of stated EC ambitions. Our data show substantial discrepancies between the inclusion of RRI concepts within official subtheme documents (e.g., on policy and work programme levels), and awareness of RRI by interviewees working on projects funded by such subthemes (see the figure). Absence of RRI keys across the majority of programme subtheme evaluation criteria is a telling example. Such evidence suggests that (i) the RRI framework is still an evolving concept, the development of which hinders its proper understanding by those who are supposed to use it; (ii) such individuals have only superficial understanding of the notion for its effective exploitation; and (iii) although the RRI framework is present on the declarative, strategic policy level (scoping and subtheme general description), it wanes in funding calls (policy operationalization) and is largely absent in evaluation criteria used in proposal assessment. Collectively, these points further suggest that applicants have little in the way of consistently aligned incentives to regard RRI as relevant in proposal design and submission. Although (i) and (ii) are primarily a matter of a lack of adequate information, awareness and training, (iii) points to limitations of European science policy efforts related to the pursuit of RRI. Such translation failures are typically caused by interplay of different logics of negotiation at the different levels ([ 11 ][12]), a linear model of innovation appealing to scientific excellence in R&I ([ 12 ][13]), actors' resistance to change, path dependencies, cognitive boundaries, and competing policy agendas ([ 13 ][14]). As the issues covered by RRI are normatively claimed to be of high relevance by political decision-makers, as evidenced in several EC documents, we conclude that the problem is one of policy integration strategy and implementation ([ 14 ][15]). The lack of clarity in conceptualizing RRI for research policy and governance, the limited understanding among key stakeholders, and the concept's conflation with other—often conflicting—policy goals (e.g., scientific excellence, economic value, technological readiness) hinder the emergence of a specific RRI-oriented policy frame ([ 15 ][16]). Such conflicting policy goals are palpable at the core of European research funding (e.g., supporting either mission-oriented innovation or curiosity-driven basic research in key funding instruments) and highlight the structural tensions between the normative ideals and potential instrumentalization ([ 3 ][3]). There are some limitations of this study that must be taken into account when interpreting results. First, the measurements were cross-sectional and though representative, are not exhaustive. Generalizability of findings could be increased if the study were to extend in a longitudinal fashion and possibly to better elaborate causal relationships among factors. Second, although we employed mixed methods in our investigation, the number of interviews and case studies could be further increased to provide additional qualitative information about the dynamics of RRI at the project level. Third, as the framework programme remains ongoing, our analysis was not able to evaluate the entire H2020 corpus. Although the results indicate evidence of patchy RRI implementation, highlighting the need for more consistent support to help align EC science policy and societal values, the progress made is nontrivial, given the history of science ([ 1 ][1]). A clear discrepancy exists between the expressed strong normative position on RRI and its integration in concrete policies and practices. Fully integrating RRI as a strong normative position into research funding and governance is a necessary but not sufficient first step to creating a working policy system that drives RRI integration. Longer-lived investments are needed for building a shared understanding and awareness of the relevance of responsibility in R&I among key stakeholders. Integrating responsibility into research funding further requires RRI to shift from a “cross-cutting issue” to a “strategic concern” that receives consistent and sustained embedding in call texts and project selection criteria. This will require “policy entrepreneurs” who can stimulate interactions across subthemes to foster alignment of RRI integration and translation. In addition, a range of integration policies are required at the system level and within subthemes, in which the issue of RRI is adopted as a goal. This is pertinent as, in case of such integration failures, it is often the normative position that is called into question instead of the implementation strategy, or actual integration pathway. The EC would benefit from enhancing previous efforts to integrate RRI and so affirm its role as a leader of ethically acceptable and societally responsible R&I on the world stage. Otherwise Europe needlessly undercuts its ability to direct research toward tackling societal challenges in ways compatible with its values. [science.sciencemag.org/content/369/6499/39/suppl/DC1][17] 1. [↵][18]1. M. Polanyi, 2. J. Ziman, 3. S. Fuller , Minerva 38, 1 (2000). [OpenUrl][19][CrossRef][20][Web of Science][21] 2. [↵][22]1. N. Mejlgaard et al ., Science 361, 761 (2018). [OpenUrl][23][FREE Full Text][24] 3. [↵][25]1. R. von Schomberg, 2. J. Hankins 1. R. von Schomberg , in International Handbook on Responsible Innovation: A Global Resource, R. von Schomberg, J. Hankins, Eds. (Edward Elgar, 2019), pp. 12–32. 4. [↵][26]1. R. Owen, 2. P. Macnaghten, 3. J. Stilgoe , Sci. Public Policy 39, 751 (2012). [OpenUrl][27][CrossRef][28][Web of Science][29] 5. [↵][30]1. D. Simon, 2. S. Kuhlmann, 3. J. Stamm, 4. W. Canzler 1. R. Owen, 2. M. Pansera , in Handbook on Science and Public Policy, D. Simon, S. Kuhlmann, J. Stamm, W. Canzler, Eds. (Edward Elgar, 2019), pp. 26–48. 6. [↵][31]DGRI, “Indicators for promoting and monitoring responsible research and innovation: Report from the expert group on policy indicators for responsible research and innovation” (Report, European Commission, 2015); [http://ec.europa.eu/research/swafs/pdf/pub\_rri/rri\_indicators\_final\_version.pdf][32]. 7. [↵][33]DGRI, Open innovation, open science, open to the world: A vision for Europe” (Directorate-General for Research and Innovation, European Union, 2016); . 8. [↵][34]1. S. Bührer et al ., “Monitoring the evolution and benefits of responsible research and innovation: Report on the researchers' survey – Study” [Report KI-1-18-886-EN-N, Directorate-General for Research; Innovation (European Commission), 2018]. 9. [↵][35]1. A. Rip , J. Responsib. Innov. 3, 290 (2016). [OpenUrl][36] 10. [↵][37]1. H. Rodríguez, 2. E. Fisher, 3. D. Schuurbiers , Res. Policy 42, 1126 (2013). [OpenUrl][38] 11. [↵][39]1. M. Howlett, 2. J. Vince, 3. P. Del Río , Politics Gov. 5, 69 (2017). [OpenUrl][40] 12. [↵][41]1. K. Rommetveit, 2. R. Strand, 3. R. Fjelland, 4. S. Funtowicz , “What can history teach us about the prospects of a European research area? Joint Research Centre scientific and policy reports” (Report JRC84065, European Commission, 2013). 13. [↵][42]1. H. Colebatch , Public Policy Admin 33, 365 (2017). [OpenUrl][43] 14. [↵][44]1. B. G. Peters et al ., Designing for Policy Effectiveness: Defining and Understanding a Concept (Cambridge Univ. Press, 2018). 15. [↵][45]1. R. Owen, 2. E.-M. Forsberg, 3. C. Shelley-Egan , “RRI-practice policy recommendations and roadmaps: Responsible research and innovation in practice” (Report, RRI-Practice Project, 2019); [www.rri-practice.eu/wp-content/uploads/2019/06/RRI-Practice\_Policy\_recommendations.pdf][46]. Acknowledgments: This project received funding from the EU's Horizon 2020 research and innovation programme under grant agreement no. 741402. We acknowledge all the consortium members who contributed to the data collection and writing of the reports (SM S11), which this study is based on. We express our gratitude to H. Tobi and N. Mejlgaard, as well as to the reviewers, for their helpful and constructive comments. 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Tracing cell trajectories in a biofilm

Science

Born in 1881 on a farm in Pennsylvania, Alice C. Evans dedicated her life to studying bacteria in dairy products. Early in her career, Alice became convinced that most bacteria display multicellular behavior as part of their life cycles. At the time, the morphological changes observed in bacterial life cycles created confusion among scientists. In 1928, as the first female president of the American Society for Microbiology, Alice wrote to the scientific community: “When one-celled organisms grow in masses, … individual cells influence and protect one another.” She continued, “Bacteriologists need not feel chagrinned … to admit that… forms they have considered as different genera are but stages in the life cycle of one species” ([ 1 ][1]). Nearly 100 years later, on page 71 of this issue, Qin et al. ([ 2 ][2]) make a substantial leap forward in deciphering cell dynamics in biofilms—groups of microorganisms that adhere to a surface, and each other, by excreting matrix components. In the interim period, microbiologists have learned that many bacteria organize in groups. This allows bacterial cells to achieve collectively what individuals in isolation cannot, thus conferring a selective advantage on the individuals. Multicellular behaviors help cells to migrate ([ 3 ][3]), resist antibiotic treatments ([ 4 ][4]), and protect themselves from predators ([ 5 ][5]). In recent years, microbiologists have begun to unravel the mechanisms behind these multicellular behaviors, by studying single-cell gene expression, growth rate regulation, and cell-to-cell interactions ([ 6 ][6]–[ 9 ][7]), as well as by developing tools to investigate the morphology and growth of entire bacterial biofilms ([ 10 ][8], [ 11 ][9]). A multicellular aggregate starts with a single founder cell that grows into a mature biofilm. Despite substantial progress, scientists still lack a detailed understanding of how bacterial cells are programmed to build multicellular structures. Each cell makes individual decisions—whether to divide, move, excrete chemicals, exert forces, or express extracellular matrix components—in response to its local environment. In turn, the local environment is determined by the collective decisions of all of its cells, played out as a mosaic over time in a three-dimensional (3D) space. A primary challenge to unraveling the mystery of how cells are programmed to produce a mature functional biofilm is that researchers lack the experimental tools needed to study how the dynamics of individual cells drive biofilm formation and structure. ![Figure][10] The building of biofilms A fountain-like flow of bacterial cells drives biofilm expansion. CREDIT: V. ALTOUNIAN/ SCIENCE In their elegant study, Qin et al. developed dual-view light-sheet microscopy to reconstruct single-cell trajectories in 3D Vibrio cholerae biofilms initiated by a single founder cell. This method fluorescently labeled cellular puncta, giving isotropic single-cell resolution in the biofilm with much less photobleaching than that seen with previous methods. This advance allowed the authors to carry out simultaneous imaging of 10,000 V. cholerae cells for the 16 hours it takes for the biofilm to develop, with 3-min intervals between subsequent images. This frequent imaging made it possible to track the trajectories of micrometer-sized cells, giving an unprecedented view into the behaviors of individual cells as the biofilm developed (see the figure). The measurements revealed a qualitative transition in an individual cell's behavior, in which Brownian motion changes to ballistic motion as the biofilm develops. This transition corresponds to a new phase of collective growth, when the biofilm as a whole begins its vertical expansion away from the substrate. In this phase, cells displayed two types of trajectories. Some of the cells expanded ballistically outward, whereas others became trapped at the substrate. Overall, these trajectories gave rise to a collective fountain-like flow, which transported some cells to the biofilm front, while bypassing the cells trapped at the substrate. This fountain-like flow allowed for fast lateral expansion of the biofilm. Cell tracking allowed Qin et al. to precisely quantify the dynamics of various cells, while also assessing how these dynamics differ for mutant cells that overproduce matrix components. To interpret the results, the authors built a mathematical model for the mechanics of biofilm expansion, balancing growth with substrate friction. By modeling different surface frictions and comparing the predicted cell motion with the observed cell motion, Qin et al. were able to explain the observed behavior as long as friction between the cells and surface was a dominant effect. This study of V. cholerae offers an exciting insight into how collective behavior can arise from processes operating at the single-cell level. The mechanisms uncovered with a gram-negative bacterial species likely will be generalizable across other bacterial types. For example, the qualitative transitions in biofilm expansion observed in this study have analogs in other bacterial biofilms. With the gram-positive bacterium Bacillus subtilis , a qualitative change in colony expansion is triggered by a cellular bistable switch in which cells expressing flagella produce extracellular matrices ([ 12 ][11], [ 13 ][12]). Osmolarity associated with matrix production drives colony expansion ([ 14 ][13]). More broadly, this study demonstrates the great potential for advances in imaging technology and computer vision to help unravel how collective behavior arises from the activity of individual cells and their interactions. However, there is much more going on inside a biofilm that cannot yet be seen. More complete information would allow researchers to not only reconstruct the motion of cells but also uncover their phenotypic states. Previous work on B. subtilis with fluorescent labeling of genetic components shows detailed spatial arrangement of various cell types, with cells carrying out different biological functions in distinct parts of the biofilm ([ 3 ][3], [ 15 ][14]). One can only hypothesize about the diversity of cellular types and functions inside the beautiful fountain revealed in the present study. A deeper understanding of bacterial multicellular behavior will increase our ability to treat bacterial infections, control natural bacterial communities, and engineer synthetic ones for specific purposes. 1. [↵][15]1. A. C. Evans , J. Bacteriol. 17, 63 (1929). [OpenUrl][16][FREE Full Text][17] 2. [↵][18]1. B. Qin et al ., Science 369, 71 (2020). [OpenUrl][19][Abstract/FREE Full Text][20] 3. 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