Hasso Plattner Institute

Tissues harbor memories of inflammation, which heighten sensitivity to diverse future assaults. Whether and how these adaptations are sustained through time and cell division remain poorly understood. We show that in mice, epidermal stem cells store lifelong, functional epigenetic records of psoriasis-like skin flares. Applying deep learning to investigate these chromatin dynamics, we unearth CpG dinucleotide density as a major driver of memory persistence. Although unnecessary for inflammation-induced transcription factors to open and establish memories, CpG-enriched sequences thereafter become essential, reinforcing accessibility across cellular generations by integrating DNA demethylation, methylation-sensitive transcription factors, sequence-intrinsic nucleosome disaffinity, and the nucleosome-destabilizing histone variant H2A.Z. Thus, once activated by inflammation-induced transcription factors, DNA sequences orchestrate persistent poise, imparting long-lasting memory to stress-sensitive genes and profoundly affecting tissue fitness upon recall.

Despite extensive theoretical research on proportionality in approval-based multiwinner voting, its impact on which committees and candidates can be selected in practice remains poorly understood. We address this gap by (i) analyzing the computational complexity of several natural problems related to the behavior of proportionality axioms, and (ii) conducting an extensive experimental study on both real-world and synthetic elections. Our findings reveal substantial variation in the restrictiveness of proportionality across instances, including previously unobserved high levels of restrictiveness in some real-world cases. We also introduce and evaluate new measures for quantifying a candidate’s importance for achieving proportional outcomes, which differ clearly from assessing candidate strength by approval score.

We introduce River, a novel Condorcet-consistent voting method that is based on pairwise majority margins and can be seen as a simplified variation of Tideman's Ranked Pairs method. River is simple to explain, simple to compute even "by hand," and gives rise to an easy-to-interpret certificate in the form of a directed tree. Like Ranked Pairs and Schulze's Beat Path method, River is a refinement of the Split Cycle method and shares with those many desirable properties, including independence of clones. Unlike the other three methods, River satisfies a strong form of resistance to agenda-manipulation that is known as independence of Pareto-dominated alternatives.

Recently, the River Method was introduced as novel refinement of the Split Cycle voting rule. The decision-making process of River is closely related to the well established Ranked Pairs Method. Both methods consider a margin graph computed from the voters' preferences and eliminate majority cycles in that graph to choose a winner. As ties can occur in the margin graph, a tiebreaker is required along with the preferences. While such a tiebreaker makes the computation efficient, it compromises the fundamental property of neutrality: the voting rule should not favor alternatives in advance. One way to reintroduce neutrality is to use Parallel-Universe Tiebreaking (PUT), where each alternative is a winner if it wins according to any possible tiebreaker. Unfortunately, computing the winners selected by Ranked Pairs with PUT is NP-complete. Given the similarity of River to Ranked Pairs, one might expect River to suffer from the same complexity. Surprisingly, we show the opposite: We present a polynomial-time algorithm for computing River winners with PUT, highlighting significant structural advantages of River over Ranked Pairs. Our Fused-Universe (FUN) algorithm simulates River for every possible tiebreaking in one pass. From the resulting FUN diagram one can then directly read off both the set of winners and, for each winner, a certificate that explains how this alternative dominates the others.

In programming, better tools often yield better results. For that, modern programming environments offer mechanisms to allow for their extensibility. The closer those tools are to the code, the easier it is for programmers to map the information provided by a tool to the code this information is about. However, existing extension mechanisms do not facilitate the close integration of tools with textual source code. Tools must be able to track program structures across edits to appear at the right positions but the parsing step of text complicates tracking structures. We propose hybrid structured editing, an approach that supports tool builders by providing structural guarantees while providing tool users with a familiar and consistent text editing interface. Hybrid structured editing allows tool builders to declare constraints on the structure that a program must conform to and ensures their observance. We present an implementation and several case studies of tools based on hybrid structured editing to demonstrate its effectiveness. Hybrid structured editing supports the safe extension of programming environments with tools that work on a structured representation of code and provide a consistent and reliable user experience.

Digital biomarkers use sensors and analytics, to offer continuous monitoring and personalized medicine. In this Perspective, we describe real-world use cases, such as glucose tracking to optimise insulin dosing and wearables to measure heart rhythms to de-risk cardiovascular trials. We also discuss the issues preventing most candidate biomarkers from reaching clinical practice. Evidence generation is costly, regulatory reviews can be redundant, commercial incentives fall short, and data silos can be biased and/or nonrepresentative. By combining harmonized qualification pathways, value-based reimbursement, modular extensions for single-trial biomarkers, and adaptive post-market evidence loops, we propose a path from experimental signal to standard of care.

National privacy laws diverge between the European Union and United States, hindering transatlantic health data exchange and slowing AI-driven medical innovation. In response, the German Ministry of Health launched the pre-competitive Data for Health initiative, leading to the BRIDGE Pilot Study (2023-2025), a researcher-led effort to address this regulatory and legal gap. Using a mixed-methods approach, including structured surveys (n = 56 expert responses), ranking of steps via relative importance indexing, and 4 Delphi meetings, experts co-developed a practical framework composed of 30 steps in 3 consecutive phases for legally compliant and technically interoperable EU-US health data collaboration. The framework emphasizes early data protection assessments, secure transfer protocols, and iterative governance checks. The final consensus framework provides a stepwise guide to navigate regulatory and legal complexities and operationalize cross-border research. Ongoing input from researchers and stakeholders will help ensure the framework remains adaptable and provides a clear, scalable foundation for cross-border health data exchange.

Automated insulin delivery (AID) systems have significantly advanced diabetes management, progressively reducing user interactions required for optimal glucose management. This review evaluates the current landscape and future potential of AID systems without meal announcement, particularly focusing on real-world insights from open-source AID (OS-AID) technologies. Although commercial AID systems operating in hybrid closed-loop (HCL) mode have improved glycaemic outcomes, they remain dependent on manual meal announcement and user-driven actions, limiting their real-world utility. Current versions of OS-AID systems, developed by the diabetes community, can allow operation without meal announcements, presenting an opportunity to move closer to truly automated diabetes management. Recent clinical trials suggest that OS-AID systems can effectively manage glucose levels without meal announcements, achieving glucose levels comparable with those obtained by AID systems in HCL mode, with the potential of reduced management burden for users. However, practical challenges persist, including the need for expert configuration and handling of rapid changes in insulin sensitivity, such as during exercise or rapid glucose fluctuations following predicted low-glucose. This review synthesises insights from user and healthcare professional experiences, and emerging clinical evidence. It highlights the fact that successful implementation of AID without meal announcement requires advanced algorithmic responsiveness, user personalisation and ongoing clinician engagement. Looking forward, integrating adjunctive therapies, artificial intelligence and enhanced physiological modelling will likely enhance system performance to drive the next generation of diabetes care towards wider adoption and true 'set-and-forget' functionality.

Digital innovations hold immense potential to transform health care delivery, particularly in sub-Saharan Africa, where financial, geographical, and infrastructural constraints continue to hinder progress toward universal health care delivery. Although a growing health tech sector offers creative solutions, few digital health interventions reach scaled implementation. In this paper, we present the digital fit/viability model-an adapted determinant framework to describe facilitators and barriers to moving from digital tools to integrated digital health implementation. We then use this model to describe the specific challenges and recommended solutions when developing digital health tools for health systems in sub-Saharan Africa.

The next generation of wireless mobile networks will be driven by software and infrastructure virtualization. At the same time, the underlying hardware will become more diverse. Edge, intermediate and central network nodes will allow distributed deployments of never seen before scale. Non-terrestrial networks, nomadic networks and networks in networks promise more dynamicity, changing their topologies over time in predictable and unpredictable ways. To handle the complexity and dynamicity, a highly flexible and adaptable network is required. The key enablers of this complex digital communication system will be programmable infrastructures. From programmable access and flexible backhauls to zero-touch, adaptable and resilient control planes, we conducted research into advanced programmability at different layers of the network, within the Open6GHub project. In this article, we present innovative solutions for advanced programmable network infrastructure and show why they are essential for our vision of the programmable infrastructure of future wireless mobile networks.