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A sensor network that is wirelessly interconnected through a dual-mode metamaterial integrated into clothing can continuously monitor systolic blood pressure in dynamic environments such as during exercise.

Energy demand and intensive computation limit the use of machine learning on-device for wearables. Here, the authors deploy edge AI in a wearable form factor to provide clinical-grade gait-based frailty assessment over weeks with no interaction required from the wearer at any point.

’Systems with long coherence times are extremely important for the processing of quantum information. To this end the authors present a system able to cool down a resonator to its quantum mechanical ground state harnessing the large coupling between an ultra-coherent mechanical resonator and a superconducting circuit.’

Vaccines and targeted therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are currently lacking. Here, the authors report a human monoclonal antibody capable of neutralizing both authentic SARS-CoV and SARS-CoV-2 by targeting a common epitope.

Although exporting hydrogen can raise domestic power costs, applying temporal matching with renewables can cut them by 31%, reduce emissions and steer welfare among exporters and consumers, offering a fairer approach to the global hydrogen trade.

Lipid droplets (LDs) supply fatty acids to cellular processes and move bidirectionally on microtubules. Here the authors show that nutrient starvation causes dispersal of mitochondria and LD to the periphery of the cell along detyrosinated microtubules and increases LD–mitochondria interactions in an AMPK-dependent manner.

The dynamics of microglia states adjacent to or far from amyloid-beta plaques are unclear. Here the authors show that non-plaque-associated microglia modulate the cell population expansion in response to amyloid deposition, and Csf1 signaling regulates their transition to the amyloid-associated state.

Video microscopy is key in studying cell migration, but accomplishing this in a high-throughput manner is still challenging. Here, the authors present an array microscope that can track the movements of thousands of individual cells simultaneously, and that can be used for drug screening studies.

The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.

Monoclonal antibodies are being used to treat a variety of autoimmune disorders, including multiple sclerosis, neuromuscular diseases and rheumatoid arthritis, but neurological adverse effects have been reported in a substantial number of patients. Bosch et al. describe the characteristics, pathogenesis and outcomes of neurological disorders associated with monoclonal antibody therapy, and make recommendations regarding their management.

The filamentous fungus expression system Thermothelomyces heterothallica (C1) is a protein expression system that may be useful for large scale antibody production. Here the authors characterise the production of a human monoclonal antibody that neutralises SARS-CoV-2 and compare functional properties in vitro and in animal models to antibodies produced using other methods.

Similarities in cancers can be studied to interrogate their etiology. Here, the authors use genome-wide association study summary statistics from six cancer types based on 296,215 cases and 301,319 controls of European ancestry, showing that solid tumours arising from different tissues share a degree of common germline genetic basis.

An E(3)-equivariant deep learning interatomic potential is introduced for accelerating molecular dynamics simulations. The method obtains state-of-the-art accuracy, can faithfully describe dynamics of complex systems with remarkable sample efficiency.

The authors introduce a machine-learning framework that predicts how materials respond to electric fields with quantum-level accuracy, capturing vibrational, dielectric, and ferroelectric behaviors up to the million-atom scale.

Mucopolysaccharidosis type IVA (MPSIVA) is a lysosomal storage disorder causing severe skeletal and non-skeletal alterations in patients. Here, the authors generate a MPSIVA rat model that mimics the disabling human pathology and develop an AAV9-Galns gene therapy to treat the disease.

Batatia and colleagues introduce a computational framework that combines message-passing networks with the atomic cluster expansion architecture and incorporates a many-body description of the geometry of molecular structures. The resulting models are interpretable and accurate.

The structure of vanadium nitrogenase reveals key differences from its counterpart molybdenum nitrogenase, particularly in the way it ligands its FeV cofactor, that help to explain the basis for the unique properties of these two nitrogenases.

Machine learning aids heterogeneous catalysis research by linking performance to physicochemical controllable properties. This Review discusses experimental and computational high-throughput and machine learning approaches, comparing them by modelling method, features, dataset size, accuracy and reaction type.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

Chromatin conformation studies are limited by the large amounts of starting material required to perform current protocols. Here the authors present Low-C, a Hi-C method for low amounts of input material and produce Low-C maps from primary B-cells of a diffuse large B-cell lymphoma patient, demonstrating the suitability of Low-C to analyse rare cell populations.

Histones have a role in antimicrobial defense. Here, the authors show that the histone H2A and the antimicrobial peptide LL-37 exert synergistic effects by enhancing bacterial membrane pores and enabling H2A entry into the bacterial cytoplasm, where it reorganizes DNA and inhibits transcription.