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.     

Gap junction remodeling and altered connexin43 expression in the failing human heart

Gap junctions (GJ) are important determinants of cardiac conduction and the evidence has recently emerged that altered distribution of these junctions and changes in the expression of their constituent connexins (Cx) may lead to abnormal coupling between cardiomyocytes and likely contribute to arrhythmogenesis. However, it is largely unknown whether changes in the expression and distribution of the major cardiac GJ protein, Cx43, is a general feature of diverse chronic myocardial diseases or is confined to some particular pathophysiological settings. In the present study, we therefore set out to investigate qualitatively and quantitatively the distribution and expression of Cx43 in normal human myocardium and in patients with dilated (DCM), ischemic (ICM), and inflammatory cardiomyopathies (MYO). Left ventricular tissue samples were obtained at the time of cardiac transplantation and investigated with immunoconfocal and electron microscopy. As compared with the control group, Cx43 labeling in myocytes bordering regions of healed myocardial infarction (ICM), small areas of replacement fibrosis (DCM) and myocardial inflammation (MYO) was found to be highly disrupted instead of being confined to the intercalated discs. In all groups, myocardium distant from these regions showed an apparently normal Cx43 distribution at the intercalated discs. Quantitative immunoconfocal analyis of Cx43 in the latter myocytes revealed that the Cx43 area per myocyte area or per myocyte volume is significantly decreased by respectively 30 and 55% in DCM, 23 and 48% in ICM, and by 21 and 40% in MYO as compared with normal human myocardium. In conclusion, focal disorganization of GJ distribution and down-regulation of Cx43 are typical features of myocardial remodeling that may play an important role in the development of an arrhythmogenic substrate in human cardiomyopathies.     

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

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Sulfate is transported at significant rates through the symbiosome membrane and is crucial for nitrogenase biosynthesis

Legume–rhizobia symbioses play a major role in food production for an ever growing human population. In this symbiosis, dinitrogen is reduced (“fixed”) to ammonia by the rhizobial nitrogenase enzyme complex and is secreted to the plant host cells, whereas dicarboxylic acids derived from photosynthetically produced sucrose are transported into the symbiosomes and serve as respiratory substrates for the bacteroids. The symbiosome membrane contains high levels of SST1 protein, a sulfate transporter. Sulfate is an essential nutrient for all living organisms, but its importance for symbiotic nitrogen fixation and nodule metabolism has long been underestimated. Using chemical imaging, we demonstrate that the bacteroids take up 20‐fold more sulfate than the nodule host cells. Furthermore, we show that nitrogenase biosynthesis relies on high levels of imported sulfate, making sulfur as essential as carbon for the regulation and functioning of symbiotic nitrogen fixation. Our findings thus establish the importance of sulfate and its active transport for the plant–microbe interaction that is most relevant for agriculture and soil fertility.     

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.     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.