Lifeboat Foundation

Interactions between RNA and proteins are the cornerstone of many important biological processes from transcription and translation to gene regulation, yet little is known about the ancient origin of said interactions. We hypothesized that peptide amyloids played a role in the origin of life and that their repetitive structure lends itself to building interfaces with other polymers through avidity. Here, we report that short RNA with a minimum length of three nucleotides binds in a sequence-dependent manner to peptide amyloids. The 3′–5′ linked RNA backbone appears to be well-suited to support these interactions, with the phosphodiester backbone and nucleobases both contributing to the affinity. Sequence-specific RNA–peptide interactions of the kind identified here may provide a path to understanding one of the great mysteries rooted in the origin of life: the origin of the genetic code.

On top of this, the use of quantification has significantly increased over the last decades with the inflation of metrics, indicators, and scores to rank and benchmark options (Muller, 2018). The case of energy policy making in the European Union is again an effective example. The European Union’s recent energy strategy has been underpinned by the Clean Energy for all Europeans packages, which are in turn supported by a number of individual directives, each one characterized by a series of quantitative goals (European Commission, 2023). The quantification of the impact (impact assessment) is customarily required to successfully promote new political measures (European Commission, 2015a) and is in turn based on quantification, often from mathematical models (Saltelli et al., 2023). The emphasis on producing exact figures to assess the contribution of a new technology, political or economic measure has put many models and their users into contexts of decision-making that at times extends beyond their original intent (Saltelli, Bammer et al., 2020). At the same time, the efforts to retrospectively assess the performance of energy models have been extremely limited, one example being the Energy Modeling Forum in the United States (Huntington et al., 1982). In spite of this, retrospective assessments can be very helpful in understanding the sources of mismatch between a forecast and the actual figures reported a posteriori (Koomey et al., 2003). For example, long-range forecast models are typically based on the assumption of gradual structural changes, which are at stake with the disruptive events and discontinuities occurring in the real world (Craig et al., 2002). This dimension is especially important in terms of the nature and pace of technology change (Bistline et al., 2023 ; Weyant & Olavson, 1999). A further critical element in this approach is the cognitive bias in scenario analysis that naturally leads to overconfidence in the option being explored and results in an underestimate of the ranges of possible outcomes (Morgan & Keith, 2008).

Additionally, in their quest for capturing the features of the energy systems represented, models have increased their complicatedness and/or complexity. In this context, the need to appraise model uncertainty has become of paramount importance, especially considering the uncertainty due to propagation errors caused by model complexification (Puy et al., 2022). In ecology, this is known as the O’Neil conjecture, which posits a principle of decreasing returns for model complexity when uncertainties come to dominate the output (O’Neill, 1989 ; Turner & Gardner, 2015). Capturing and apportioning uncertainty is crucial for a healthy interaction at the science–policy interface, including energy policy making, because it promotes better informed decision-making. Yet Yue et al. (2018) found that only about 5% of the studies covering energy system optimization models have included some form of assessment of stochastic uncertainty, which is the part of uncertainty that can be fully quantified (Walker et al., 2003). When it comes to adequately apportioning this uncertainty onto the input parameters and hypotheses through sensitivity analysis, the situation is even more critical: Only very few papers in the energy field have made the use of state-of-the-art approaches (Lo Piano & Benini, 2022 ; Saltelli et al., 2019). Further to that, the epistemic part of uncertainty, the one that arises due to imperfect knowledge and problem framing, has been largely ignored in the energy modeling literature (Pye et al., 2018). For instance, important sources of uncertainties associated with regulatory lag and public acceptance have typically been overlooked. 1

In this contribution, we discuss three approaches to deal with the challenges of non-neutrality and uncertainty in models: The numerical unit spread assessment pedigree (NUSAP) method, diagnostic diagrams, and sensitivity auditing (SAUD). These challenges are especially critical when only one (set of) model(s) has been selected to contribute to decision-making. One practical case is used to showcase in retrospective the relevance of the issue and the associated problems: the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s.

Department of Energy user facility helps probe questions from changes in the structure of nuclei to nuclear reactions that shape the Universe.

The Facility for Rare Isotope Beams (FRIB) enables discoveries in the science of atomic nuclei, their role in the cosmos, and the fundamental symmetries of nature. This accelerator facility uses beams of short-lived nuclei not available elsewhere. Results from FRIB address questions such as the limits of the nuclear chart, the origin of the elements, and the reason for why there is more matter than antimatter in our Universe.

In FRIB’s first year, its measurements tackled the changes in the structure of the shortest-lived nuclei, exotic decay modes, nuclear reactions that affect cosmic events such as X-ray bursts, and processes in the crusts of neutron stars.

A glowing horizon for phones.

Companies like OpenAI and Midjourney have opened Pandora’s box, opening them up to considerable legal trouble by training their chatbots on the vastness of the internet while largely turning a blind eye to copyright.

As professor and author Gary Marcus and film industry concept artist Reid Southen, who has worked on several major films for the likes of Marvel and Warner Brothers, argue in a recent piece for IEEE Spectrum, tools like DALL-E 3 and Midjourney could land both companies in a “copyright minefield.”

It’s a heated debate that’s reaching fever pitch. The news comes after the New York Times sued Microsoft and OpenAI, alleging it was responsible for “billions of dollars” in damages by training ChatGPT and other large language models on its content without express permission. Well-known authors including “Game of Thrones” author George RR Martin and John Grisham recently made similar arguments in a separate copyright infringement case.

DNA obtained from the bones and teeth of ancient Europeans who lived up to 34,000 years ago is providing insight into the origin of the often-disabling neurological disease multiple sclerosis, finding that genetic variants that now increase its risk once served to protect people from animal-borne diseases.

‘Mechanism’ is a frequently used causal concept in neuroscience but can have different meanings that are often not specified. In this Review, Ross and Bassett explore these different meanings and the challenges associated with the variable usage of this term before discussing how these challenges may be met.

Join the discussion on this paper page.

Stanford Medicine scientists used transcranial magnetic stimulation to temporarily enhance hypnotizability in patients with chronic pain, making them better candidates for hypnotherapy.

How deeply someone can be hypnotized — known as hypnotizability — appears to be a stable trait that changes little throughout adulthood, much like personality and IQ. But now, for the first time, Stanford Medicine researchers have demonstrated a way to temporarily heighten hypnotizablity — potentially allowing more people to access the benefits of hypnosis-based therapy.

In the new study, published on January 4 in Nature Mental Health, the researchers found that less than two minutes of electrical stimulation targeting a precise area of the brain could boost participants’ hypnotizability for about one hour.