News & Views

The vulnerability of quantum machine learning is demonstrated on a superconducting quantum computer, together with a defense strategy based on noisy intermediate-scale quantum (NISQ) adversarial learning.

Recent work uses a language model to gain insight into how the human brain understands the combined meaning of words in a sentence, and uncovers parts of the brain that contribute to this understanding.

The modeling of non-linear morphological changes in biological systems is a challenging task. Motivated by the observation of exotic pattern formation processes on fruit surfaces, a chiral wrinkling topology is disclosed as a mechanical structural instability, which is then exploited for the design of enhanced adaptive graspers.

The simulation of relativistic flows that can transit from a fluid-like to a gas-like substance poses challenges for computational methods. A lattice kinetic scheme is proposed to simulate such flows, which allows a computational probe of both strongly and weakly interacting regimes.

In a recent study a phenomenological model was used to study the effects of activity-dependent myelination (ADM) on network activity and information transmission in the brain. The model explores how the conduction velocity of an axon — and thus the overall transmission delay — varies as a function of neural activity.

A recent study proposes a metric to quantify how much information different types of epidemiological surveillance data, such as case counts and death counts, convey about the real-time transmission of an epidemic.

Integrating social mixing data into epidemic models can help policy makers better understand epidemic spread. However, empirical mixing data might not be immediately available in most populations. In a recent work, a network model methodology is proposed to construct micro-level social mixing structure when empirical data are not available.

A new Bayesian analysis of remote work data supports one of the oldest theories in social networks, with fresh implications for the future of work environments.

An algorithm is presented for the simulation of reaction–diffusion systems on complex geometries, providing insight on how the interplay of cell geometry and biochemistry can control polarity in living cells.

Determining how information flows throughout a network of interconnected components is a challenging task in many scientific domains. A framework is presented to deconstruct the flow of signals that are transmitted across any two areas (such as brain areas) and define how each area represents these signals.

The problem of automatically determining state variables for physical systems is challenging, but essential in the modeling process of almost all scientific and engineering processes. A deep neural network-based approach is proposed to find state variables for systems whose data are given as video frames.

The design of protein sequences that can precisely fold into pre-specified 3D structures is a challenging task. A recently proposed deep-learning algorithm improves such designs when compared with traditional, physics-based protein design approaches.

A graph neural network-based tool is introduced to perform unsupervised cell clustering using spatially resolved transcriptomics data that can uncover cell identities, interactions, and spatial organization in tissues and organs.

Aptamers are expected to be next-generation drugs, but identifying candidate aptamers is a challenging task given the large search space. Now, an artificial intelligence (AI)-powered tool called RaptGen is proposed for improving the successful identification of aptamer sequences.

Characterizing the brain’s connectome at multiple scales is essential for unraveling fundamental principles of cortical information processing and how it impacts behavior. A GPU-based implementation for connectome pruning is proposed, achieving greater than 100-fold speedups over previous CPU-based implementations.

The identification of robust and generalizable biomarkers based on microbial abundance data is a challenging task. An algorithm shows an enhanced classification performance by quantifying shifts in microbial co-abundances.

Variational Monte Carlo is one of the most accurate methods to solve the many-electron Schrödinger equation, but suffers from high computational cost. A recent study uses a weight-sharing technique to accelerate the neural network-based variational Monte Carlo method, allowing accurate and effective simulations of molecules.

A dynamic model of SARS-CoV-2 transmission is integrated with a 63-sector economic model to identify control strategies for optimizing economic production while keeping schools and universities operational, and for constraining infections such that emergency hospital capacity is not exceeded.

A robust and reliable codec is the backbone for any digital DNA storage. A recent work introduces a codec based on ancient Chinese philosophy, yin–yang, that outperforms other codecs in terms of reliability and physical information density.

Predicting the risk of acute graft-versus-host-disease after transplantation is challenging due to the presence of multimodal data and continuous evolution of disease states. A dynamic probabilistic algorithm has recently been proposed to address these challenges.