The breakthrough paradox: How focusing on one form of innovation jeopardizes the advancement of science

Research needs a balance of risk‐taking in “breakthrough projects” and gradual progress. For building a sustainable knowledge base, it is indispensable to provide support for both. Subject Categories: Careers, Economics, Law & Politics, Science Policy & Publishing

Science is about venturing into the unknown to find unexpected insights and establish new knowledge. Increasingly, academic institutions and funding agencies such as the European Research Council (ERC) explicitly encourage and support scientists to foster risky and hopefully ground‐breaking research. Such incentives are important and have been greatly appreciated by the scientific community. However, the success of the ERC has had its downsides, as other actors in the funding ecosystem have adopted the ERC’s focus on “breakthrough science” and respective notions of scientific excellence. We argue that these tendencies are concerning since disruptive breakthrough innovation is not the only form of innovation in research. While continuous, gradual innovation is often taken for granted, it could become endangered in a research and funding ecosystem that places ever higher value on breakthrough science. This is problematic since, paradoxically, breakthrough potential in science builds on gradual innovation. If the value of gradual innovation is not better recognized, the potential for breakthrough innovation may well be stifled.

While continuous, gradual innovation is often taken for granted, it could become endangered in a research and funding ecosystem that places ever higher value on breakthrough science.

Concerns that the hypercompetitive dynamics of the current scientific system may impede rather than spur innovative research have been voiced for many years (Alberts et al, 2014). As performance indicators continue to play a central role for promotions and grants, researchers are under pressure to publish extensively, quickly, and preferably in high‐ranking journals (Burrows, 2012). These dynamics increase the risk of mental health issues among scientists (Jaremka et al, 2020), dis‐incentivise relevant and important work (Benedictus et al, 2016), decrease the quality of scientific papers (Sarewitz, 2016) and induce conservative and short‐term thinking rather than risk‐taking and original thinking required for scientific innovation (Alberts et al, 2014; Fochler et al, 2016). Against this background, strong incentives for fostering innovative and daring research are indispensable.     

Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics

The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018–2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5–10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.     

Reconfiguration of a Multi-oscillator Network by Light in the Drosophila Circadian Clock

1. Download high-res image (157KB)
2. Download full-size image

     

Tertiary motifs as building blocks for the design of protein‐binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.     

Rapid parasite adaptation drives selection for high recombination rates

The Red Queen hypothesis proposes that sex is maintained through selection pressure imposed by coevolving parasites: susceptible hosts are able to escape parasite pressure by recombining their genome to create resistant offspring. However, previous theoretical studies have shown that the Red Queen typically selects against sex unless selection is strong, arguing that high rates of recombination cannot evolve when parasites are of low virulence. Here we show that under the biologically plausible assumption of a severe fitness cost for parasites that fail to infect, the Red Queen can cause selection for high recombination rates, and that the strength of virulence is largely irrelevant to the direction of selection for increased recombination rates. Strong selection on parasites and short generation times make parasites usually better adapted to their hosts than vice versa and can thus favor higher recombination rates in hosts. By demonstrating the importance of host-imposed selection on parasites, our findings resolve previously reported conflicting results.     

Detecting Differential Transmissibilities That Affect the Size of Self-Limited Outbreaks

Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying ) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in , suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission.