Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Encoding and downsampling images is key for visual prostheses. Here, the authors show that an actor-model framework using the inherent computation of the retinal network yields better performance in downsampling images compared to learning-free methods.

    • Franklin Leong
    • , Babak Rahmani
    •  & Diego Ghezzi

  • Article
    | Open Access

    Global challenges demand global solutions. Here, the authors show a distributed self-driving lab architecture in The World Avatar, linking robots in Cambridge and Singapore for asynchronous multi-objective reaction optimisation.

    • Jiaru Bai
    • , Sebastian Mosbach
    •  & Markus Kraft

  • Article
    | Open Access

    Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.

    • Jun Ma
    • , Yuting He
    •  & Bo Wang

  • Article
    | Open Access

    In this work, the authors report the FreeDTS software to simulate biomembranes at the mesoscale. The software provides various membrane simulations, focusing on protein organization and shape remodeling. A versatile tool propelling realistic membrane studies and diverse applications.

    • Weria Pezeshkian
    •  & John H. Ipsen

  • Article
    | Open Access

    Cryo-EM is the go-to method for visualizing large, flexible biomolecules. Here, authors introduce a new Gaussian mixture modelling method for cryo-EM modelling tasks, including refinement, composite map generation and ensemble representation.

    • Joseph G. Beton
    • , Thomas Mulvaney
    •  & Maya Topf

  • Article
    | Open Access

    It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

    • Junyu Liu
    • , Minzhao Liu
    •  & Liang Jiang

  • Article
    | Open Access

    While federated learning is promising for efficient collaborative learning without revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high communication overhead, and struggles to adapt to heterogeneous models. Here, the authors show a federated distillation method to tackle these challenges, which leverages the strengths of knowledge distillation in a federated learning setting.

    • Jiawei Shao
    • , Fangzhao Wu
    •  & Jun Zhang

  • Article
    | Open Access

    Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

    • Zhijian Yang
    • , Junhao Wen
    •  & Christos Davatzikos

  • Article
    | Open Access

    Network structures can be examined at different scales, and subnetworks in the form of motifs can provide insights into global network properties. The authors propose an approach to decompose a network into a set of latent motifs, which can be used for network comparison, network denoising, and edge inference.

    • Hanbaek Lyu
    • , Yacoub H. Kureh
    •  & Mason A. Porter

  • Article
    | Open Access

    Near-eye displays are pivotal for building augmented and virtual reality platforms, but hurdles remain in achieving comfort and realistic visual experiences. Here, authors demonstrate compact 3D holographic glasses with focus cues by combining merits of waveguide displays and holographic displays.

    • Changwon Jang
    • , Kiseung Bang
    •  & Douglas Lanman

  • Article
    | Open Access

    Generating microfluidic droplets with application-specific desired characteristics is hard. Here the authors report fluid-agnostic machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable single and double emulsions.

    • Ali Lashkaripour
    • , David P. McIntyre
    •  & Polly M. Fordyce

  • Article
    | Open Access

    Neural wavefunctions have become a highly accurate approach to solve the Schrödinger equation. Here, the authors propose an approach to optimize for a generalized wavefunction across compounds, which can help developing a foundation wavefunction model.

    • Michael Scherbela
    • , Leon Gerard
    •  & Philipp Grohs

  • Article
    | Open Access

    Designing efficient artificial neural network circuit architectures for optimal information routing remains a challenge. Here, the authors propose “Mosaic", the first demonstration of on-chip in-memory spike routing using memristors, optimized for small-world graphs prevalent in mammalian brains, offering orders of magnitude reduction in routing events compared to current approaches.

    • Thomas Dalgaty
    • , Filippo Moro
    •  & Melika Payvand

  • Article
    | Open Access

    Existing feature visualisation methods are not well-suited for regression tasks. Here, authors introduce a method to learn the manifold topology related to deep neural network output and target labels and provide insightful visualisations of the high-dimensional features while preserving the local geometry.

    • Md Tauhidul Islam
    • , Zixia Zhou
    •  & Lei Xing

  • Article
    | Open Access

    Antibody Mediated Prevention (AMP) trials showed that the broadly neutralizing antibody VRC01 could prevent some HIV-1 acquisitions. Here the authors use VRC01 levels and the sensitivity of each acquired HIV virus to predict viral loads in the AMP studies and show that VRC01 influenced viral loads, though potency was lower in vivo than expected.

    • Daniel B. Reeves
    • , Bryan T. Mayer
    •  & Srilatha Edupuganti

  • Article
    | Open Access

    Developing efficient reservoir computing hardware that combines optically excited acoustic and spin waves with high spatial density remains a challenge. In this work, the authors propose a design capable of recognizing visual shapes drawn by a laser within remarkably confined spaces, down to 10 square microns.

    • Dmytro D. Yaremkevich
    • , Alexey V. Scherbakov
    •  & Manfred Bayer

  • Article
    | Open Access

    Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Here, the authors propose a bio-inspired artificial neural network hardware able to self-adapt to solve new complex tasks, by autonomously connecting nodes using electropolymerization.

    • Kamila Janzakova
    • , Ismael Balafrej
    •  & Fabien Alibart

  • Article
    | Open Access

    Abrupt regime shifts could in theory be predicted from early warning signals. Here, the authors show that true critical transitions are challenging to classify in lake planktonic systems, due to mismatches between trophic levels, and reveal uneven performance of early warning signal detection methods.

    • Duncan A. O’Brien
    • , Smita Deb
    •  & Christopher F. Clements

  • Article
    | Open Access

    Using AI to predict disease can improve interventions slow down or prevent disease. Here, the authors show that generative AI models built on the framework of Transformer, the model that also empowers ChatGPT, can achieve state-of-the-art performance on disease predictions based on longitudinal electronic records.

    • Zhichao Yang
    • , Avijit Mitra
    •  & Hong Yu

  • Article
    | Open Access

    Scintillators are widely used for radiation detection and require proper calibration in such applications. Here the authors discuss a Bayesian inference and machine learning method in combination with the Compton-edge probing that can describe the non-proportional scintillation response of inorganic scintillators.

    • David Breitenmoser
    • , Francesco Cerutti
    •  & Sabine Mayer

  • Article
    | Open Access

    Acute GVHD severity grading is based on target organ assessments. Here, the authors show that data-driven grading can identify 12 distinct grades with specific aGVHD phenotypes, which are associated with clinical outcomes, and that their method outperformed conventional gradings.

    • Evren Bayraktar
    • , Theresa Graf
    •  & Amin T. Turki

  • Article
    | Open Access

    The modelling of human-like behaviours is one of the challenges in the field of Artificial Intelligence. Inspired by experimental studies of cultural evolution, the authors propose a reinforcement learning approach to generate agents capable of real-time  third-person imitation.

    • Avishkar Bhoopchand
    • , Bethanie Brownfield
    •  & Lei M. Zhang

  • Article
    | Open Access

    The usual treatment of wave scattering theory relies on a formalism that does not easily allow for probing optimal spectral response. Here, the authors show how an alternative formalism, encoding fundamental principles of causality and passivity, can be used to make sense of complex scattered fields’ structures.

    • Lang Zhang
    • , Francesco Monticone
    •  & Owen D. Miller

  • Article
    | Open Access

    Embedding of complex networks in the latent geometry allows for a better understanding of their features. The authors propose a framework for mapping complex networks into high-dimensional hyperbolic space to capture their intrinsic dimensionality, navigability and community structure.

    • Robert Jankowski
    • , Antoine Allard
    •  & M. Ángeles Serrano

  • Article
    | Open Access

    Novel indicators of infectious disease prevalence could improve real-time surveillance and support healthcare planning. Here, the authors show that sales data for non-prescription medications from a UK high street retailer can improve the accuracy of models forecasting mortality from respiratory infections.

    • Elizabeth Dolan
    • , James Goulding
    •  & Laila J. Tata

  • Article
    | Open Access

    There is a need for dataset-dependent MS2 acquisition in trapped ion mobility spectrometry imaging. Here the authors report spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) which enables on-tissue metabolite and lipid annotation in mass spectrometry bioimaging studies, and use this to visualise the chemical space in rat brains.

    • Steffen Heuckeroth
    • , Arne Behrens
    •  & Robin Schmid

  • Article
    | Open Access

    Studies of the evolution of cooperation often assume information use that is inconsistent with empirical observations. Here, the authors’ research on general imitation dynamics reveals that cooperation is fostered by individuals using less personal information and more social information.

    • Xiaochen Wang
    • , Lei Zhou
    •  & Aming Li

  • Article
    | Open Access

    In this study, the authors develop a mathematical modelling framework to estimate the impacts of non-pharmaceutical interventions and vaccination on COVID-19 incidence. The model accounts for changes in SARS-CoV-2 variant and population immunity, and here they use it to investigate epidemic dynamics in French Polynesia.

    • Lloyd A. C. Chapman
    • , Maite Aubry
    •  & Adam J. Kucharski

  • Article
    | Open Access

    Many real-world systems are characterized by bursty dynamics with interchanging periods of intense activity and quiescence. The authors propose a method to construct temporal networks that match a given activity pattern, and apply it to empirical bursty patterns.

    • Anzhi Sheng
    • , Qi Su
    •  & Joshua B. Plotkin

  • Article
    | Open Access

    Pseudotime analysis is prevalent in single-cell RNA-seq, but it remains challenging to perform it across multiple samples and experimental conditions. Here, the authors develop Lamian, a computational framework for multi-sample pseudotime analysis that adjusts for biological and technical variation to detect gene program changes along cell trajectories and across conditions.

    • Wenpin Hou
    • , Zhicheng Ji
    •  & Hongkai Ji

  • Article
    | Open Access

    Ecosystems must be able to bounce back from perturbations to persist without species extinctions. This study uses theoretical modelling to show the importance of reactivity—how species respond in the short term to perturbations—for assessing the health of complex ecosystems, revealing that it can be a better predictor of extinction risk than stability.

    • Yuguang Yang
    • , Katharine Z. Coyte
    •  & Aming Li

  • Article
    | Open Access

    Optoelectronic neural networks are a promising avenue in AI computing for parallelization, power efficiency, and speed. Here, the authors present a dual-neuron optical-artificial learning approach for training large-scale diffractive neural networks, achieving VGG-level performance on ImageNet in simulation with a network that is 10 times larger than existing ones.

    • Xiaoyun Yuan
    • , Yong Wang
    •  & Lu Fang

  • Article
    | Open Access

    In order to be useful for future large-scale quantum computing, quantum error correction needs to allow for fast enough classical decoding time, while at the moment the slowdown is exponential in the size of the code. Here, the authors remove this roadblock, showing how to parallelize decoding and make the slowdown polynomial.

    • Luka Skoric
    • , Dan E. Browne
    •  & Earl T. Campbell

  • Article
    | Open Access

    Over their careers, medicinal chemists develop a gut feeling for what is a promising molecule. Here, the authors use machine learning models to learn this intuition and show that it can be successfully applied in several drug discovery scenarios.

    • Oh-Hyeon Choung
    • , Riccardo Vianello
    •  & José Jiménez-Luna

  • Article
    | Open Access

    Rapid adoption of zero-emission vehicles with a concurrent transition to clean electricity is essential to achieve U.S. transportation decarbonization goals. Managing travel demand can ease this transition by reducing the need for clean electricity supply. @cghoehne, @nrel, #NRELMobility

    • Christopher Hoehne
    • , Matteo Muratori
    •  & Ookie Ma

  • Article
    | Open Access

    Studies on mutant invasion typically assume populations in isolation, rather than part of an ecological community. Here, the authors use computational models to investigate how enemy-victim interactions influence properties of mutant invasion, showing that selection is substantially weakened.

    • Dominik Wodarz
    •  & Natalia L. Komarova

  • Article
    | Open Access

    Physical unclonable functions (PUFs) normally ensure authentication of small physical objects. Here, instead, the authors observe that also rooms and buildings can serve as PUFs. They apply this insight to monitor the integrity of enclosed environments, such as art galleries, bank vaults, or data centers.

    • Johannes Tobisch
    • , Sébastien Philippe
    •  & Ulrich Rührmair

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