Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Robust genome-wide association study (GWAS) methods that can utilise time-to-event information such as age-of-onset will help increase power in analyses for common health outcomes. Here, the authors propose a computationally efficient time-to-event model for GWAS.

    • Emil M. Pedersen
    • , Esben Agerbo
    •  & Bjarni J. Vilhjálmsson

  • Article
    | Open Access

    Fano varieties are mathematical shapes that are basic units in geometry, they are challenging to classify in high dimensions. The authors introduce a machine learning approach that picks out geometric structure from complex mathematical data where rigorous analytical methods are lacking.

    • Tom Coates
    • , Alexander M. Kasprzyk
    •  & Sara Veneziale

  • Article
    | Open Access

    Here, the reaction of the suicide inhibitor sulbactam with the M. tuberculosis β-lactamase (BlaC) is investigated with time-resolved crystallography. Singular Value Decomposition is implemented to extract kinetic information despite changes in unit cell parameters during the time-course of the reaction.

    • Tek Narsingh Malla
    • , Kara Zielinski
    •  & Marius Schmidt

  • Article
    | Open Access

    Modern microscopes can image a sample with sub-Angstrom and sub-picosecond resolutions, but this often requires analysis of tremendously large datasets. Here, the authors demonstrate that an autonomous experiment can yield over a 70% reduction in dataset size while still producing high-fidelity images of the sample.

    • Saugat Kandel
    • , Tao Zhou
    •  & Mathew J. Cherukara

  • Article
    | Open Access

    Image stitching in fluorescence microscopy can be a hindrance to image quality and to downstream quantitative analyses. Here, the authors propose a deep learning-based stripe self-correction method that corrects diverse stripes and artifacts for stitched microscopic images.

    • Shu Wang
    • , Xiaoxiang Liu
    •  & Jianxin Chen

  • Article
    | Open Access

    Security proofs against general attacks are the ultimate goal of QKD. Here, the authors show how the Generalised Entropy Accumulation Theorem can be used, for some classes of QKD scenarios, to translate security proofs against collective attacks in the asymptotic regime into proofs against general attacks in the finite-size regime.

    • Tony Metger
    •  & Renato Renner

  • Article
    | Open Access

    Personal communication networks through mobile phones and online platforms can be characterized by patterns of tie strengths. The authors propose a model to explain driving mechanisms of emerging tie strength heterogeneity in social networks, observing similarity of patterns across various datasets.

    • Gerardo Iñiguez
    • , Sara Heydari
    •  & Jari Saramäki

  • Article
    | Open Access

    Many expression deconvolution approaches have been developed to estimate % RNA contributions of diverse cell types to mixed RNA measurements. Here, the authors have developed a complementary approach called scProjection to recover cell type-specific expression profiles from mixed RNA measurements.

    • Nelson Johansen
    • , Hongru Hu
    •  & Gerald Quon

  • Article
    | Open Access

    Authors utilize a number of models (mathematical, in vitro and in vivo infection) to analyse pre-clinical and Phase I clinical trial data, in regard to potential risk of resistance associated with a Plasmodium falciparum inhibitor, cabamiquine.

    • Eva Stadler
    • , Mohamed Maiga
    •  & Thomas Spangenberg

  • Article
    | Open Access

    Here, Mei and Chen propose an in-memory mechanical computing architecture with simplified and reduced data exchange, where computing occurs within mechanical memory units, to facilitate the design of intelligent mechanical systems.

    • Tie Mei
    •  & Chang Qing Chen

  • Article
    | Open Access

    Inspired by human analogical reasoning in cognitive science, the authors propose an approach combining deep learning systems with an analogical reasoning mechanism, to detect abstract similarity in real-world images without intensive training in reasoning tasks.

    • Taylor Webb
    • , Shuhao Fu
    •  & Hongjing Lu

  • Comment
    | Open Access

    The current gap between computing algorithms and neuromorphic hardware to emulate brains is an outstanding bottleneck in developing neural computing technologies. Aimone and Parekh discuss the possibility of bridging this gap using theoretical computing frameworks from a neuroscience perspective.

    • James B. Aimone
    •  & Ojas Parekh

  • Perspective
    | Open Access

    Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.

    • Herbert Jaeger
    • , Beatriz Noheda
    •  & Wilfred G. van der Wiel

  • Perspective
    | Open Access

    The design of polymers for regenerative medicine could be accelerated with the help of machine learning. Here the authors note that machine learning has been applied successfully in other areas of polymer chemistry, while highlighting that data limitations must be overcome to enable widespread adoption within polymeric biomaterials.

    • Samantha M. McDonald
    • , Emily K. Augustine
    •  & Matthew L. Becker

  • Article
    | Open Access

    The serial interval (time between symptom onset in an infector and infectee) is usually estimated from contact tracing data, but this is not always available. Here, the authors develop a method for estimation of serial intervals using whole genome sequencing data and apply it data from clusters of SARS-CoV-2 in Victoria, Australia.

    • Jessica E. Stockdale
    • , Kurnia Susvitasari
    •  & Caroline Colijn

  • Article
    | Open Access

    Inferring co-expressions with scRNA-seq data is challenging, and existing methods suffer from inflated false positives and biases. Here, the authors proposed CS-CORE, which yields unbiased estimates and identifies co-expressions that are more reproducible and biologically relevant for scRNA-seq data.

    • Chang Su
    • , Zichun Xu
    •  & Jingfei Zhang

  • Article
    | Open Access

    Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems; however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.

    • Peter Y. Lu
    • , Rumen Dangovski
    •  & Marin Soljačić

  • Article
    | Open Access

    Synchronization of e-wearables can be challenging due to device performance variations. Here, the authors develop a general neural network-based solution that analyses and correct disparities between multiple virtual clocks and demonstrate it for a Bluetooth synchronized motion capture system at high frequency.

    • Karthikeyan Kalyanasundaram Balasubramanian
    • , Andrea Merello
    •  & Marco Crepaldi

  • Article
    | Open Access

    While experts analyze cytomorphology to diagnose myelodysplastic syndromes, definitive diagnosis requires complementary information such as karyotype and molecular genetics testing. Here, the authors present a computational method that automatically detects, characterizes and helps identify blood cell characteristics associated with this group of diseases.

    • José Guilherme de Almeida
    • , Emma Gudgin
    •  & Moritz Gerstung

  • Article
    | Open Access

    Diagnosing shortcut learning in clinical models is difficult, as sensitive attributes may be causally linked with disease. Using multitask learning, the authors propose a method to directly test for the presence of shortcut learning in clinical ML systems.

    • Alexander Brown
    • , Nenad Tomasev
    •  & Jessica Schrouff

  • Article
    | Open Access

    In biology, individuals are known to achieve higher navigation accuracy when moving in a group compared to single animals. The authors show that simple self-propelled robotic modules that are incapable of accurate motion as individuals can achieve accurate group navigation once coupled via deformable elastic links.

    • Federico Pratissoli
    • , Andreagiovanni Reina
    •  & Roderich Groß

  • Article
    | Open Access

    Recent advances in multi-condition single-cell multi-cohort studies enable exploration of diverse cell states. Here, authors present scMerge2, an algorithm that allows integration of a large COVID-19 data collection with over five million cells to uncover distinct signatures of disease progression.

    • Yingxin Lin
    • , Yue Cao
    •  & Jean Y. H. Yang

  • Article
    | Open Access

    Rare quantum tunneling two-level systems are known to govern the glass physics at low temperatures, but it remains challenging to detect them in simulations. Ciarella et al. show a machine learning approach to efficiently identify the structural defects, allowing to predict the quantum splitting.

    • Simone Ciarella
    • , Dmytro Khomenko
    •  & Francesco Zamponi

  • Article
    | Open Access

    In stochastic games, there is a feedback loop between a group’s social behaviors and its environment. Kleshnina et al. show that groups are often more cooperative when they know the exact state of their environment, although there are also intriguing cases when ignorance is beneficial.

    • Maria Kleshnina
    • , Christian Hilbe
    •  & Martin A. Nowak

  • Article
    | Open Access

    Generalization - that is, the ability to extrapolate from training data to unseen data - is fundamental in machine learning, and thus also for quantum ML. Here, the authors show that QML algorithms are able to generalise the training they had on a specific distribution and learn over different distributions.

    • Matthias C. Caro
    • , Hsin-Yuan Huang
    •  & Zoë Holmes

  • Article
    | Open Access

    Accurate prediction of peptidic hydrogels could prove useful for diverse biomedical applications. Here, the authors develop a “human-in-the-loop” approach that integrates coarse-grained molecular dynamics, machine learning, and experimentation to design natural peptide hydrogels.

    • Tengyan Xu
    • , Jiaqi Wang
    •  & Huaimin Wang

  • Article
    | Open Access

    The authors show that the ramified ductal network of the mouse salivary gland develops from a set of simple probabilistic rules, where ductal elongation and branching are driven by the persistent expansion of the surrounding tissue.

    • Ignacio Bordeu
    • , Lemonia Chatzeli
    •  & Benjamin D. Simons

  • Article
    | Open Access

    The authors propose a confocal complemented signal-object collaborative regularization method for non-line-of-sight (NLOS) imaging without specific requirements on the spatial pattern of measurement points. The method extends the application range of NLOS imaging.

    • Xintong Liu
    • , Jianyu Wang
    •  & Lingyun Qiu

  • Article
    | Open Access

    The sparse, noisy, and distorted raw photon data captured by single-photon cameras make it difficult to estimate scene properties under challenging illumination conditions. Here, the authors present Collaborative photon processing for Active Single-Photon Imaging (CASPI), a technology-agnostic, application-agnostic, and training-free photon processing pipeline for high-resolution single-photon cameras.

    • Jongho Lee
    • , Atul Ingle
    •  & Mohit Gupta

  • Article
    | Open Access

    Li-ion batteries are used to store energy harvested from photovoltaics. However, battery use is sporadic and standard diagnostic methods cannot be applied. Here, the authors propose a methodology for diagnosing photovoltaics-connected Li-ion batteries that use trained machine learning algorithms.

    • Matthieu Dubarry
    • , Nahuel Costa
    •  & Dax Matthews

  • Article
    | Open Access

    Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Here, the authors present single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer cell type-specific GRN dynamics from scRNA-seq and scATAC-seq datasets collected for diverse cell fate specification trajectories.

    • Shilu Zhang
    • , Saptarshi Pyne
    •  & Sushmita Roy

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