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SeeDB-Live is a tissue-clearing approach for live samples such as tissue slices or the in vivo brain. It improves image quality while having minimal effects on electrophysiological properties of neurons.

Quantum computers often exhibit a bias in the type of error that is the most common or severe. Entanglement has now been demonstrated for qubits encoded with an error correction code that is designed to efficiently handle biased errors.

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.

Non-contact thermometry is one of the key technologies for modern science and industry. Here, authors demonstrated measurement of temperature of an element using neutron resonance spectroscopy with Doppler broadening with single intense short neutron pulse provided from high peak power.

Humanity’s Last Exam, a multi-modal benchmark at the frontier of human knowledge, is designed to be an expert-level closed-ended academic benchmark with broad subject coverage.

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.

Granulosa cells are the key lineage driving continuous folliculogenesis. Here, the authors reveal that the transcription factor Fosl2 reconfigures chromatin state in these cells and coordinates vital signalling pathways for ovulation and reproduction.

Hybrid density functionals are crucial for accurate materials calculations, yet their application is limited by the computational cost. Here, authors overcome this efficiency bottleneck through deep learning, enabling large-scale hybrid density functional calculations.

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.

Noise-induced synchronization is known in classical systems and has recently been proposed in quantum many-body settings. Here, the authors experimentally demonstrate stable and entangled synchronized oscillations at the ends of a superconducting qubit chain by applying Gaussian noise to a single qubit.

This Review explores the integration of deep learning in first-principles electronic structure calculations, addressing the accuracy–efficiency dilemma of traditional algorithms and extending first-principles methods to unprecedented scales and complexity.

Using sequencing and haplotype-resolved assembly of 65 diverse human genomes, complex regions including the major histocompatibility complex and centromeres are analysed.

The correlation between the instability and facet properties of wide-bandgap perovskites has not been revealed. Here, the authors introduce trioctylphosphine oxide for strong oriented growth of (111) facet in films and achieve certified efficiency of 30.26% for perovskite/silicon tandem solar cells.

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

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.

Enzyme kinetic parameter prediction is a challenge in enzyme discovery and engineering. Here, the authors train a robust deep learning model CataPro to predict enzyme kinetic parameters and validate its practicality through wet-lab experiments.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

Using in vivo models and human cancer datasets, Yang and colleagues identify a role for the epigenetic regulator Rnf2 in repressing the antitumor immune responses of natural killer and CD4⁺ T cells.

Tumor loss of IFN-γ signalling is a major mechanism of resistance to immune checkpoint blockers. Here the authors report that melanoma cells with knockout of IFNγR1 show constitutive JAK1/2 activation and that the JAK1/2 inhibitor ruxolitinib can overcome resistance to anti-CTLA-4 therapy.

Computer-aided drug design has a high computational cost and a newly identified drug candidate might be unsuitable due to a range of drug properties. Lam and colleagues trained a model based on graph convolutional variational encoders that predicts a range of properties simultaneously to accelerate virtual screening.

Structural variants (SVs) in human genomes contribute diversity and diseases. Here, the authors use a multi-platform strategy to generate haplotype-resolved SVs for three human parent–child trios.

The novoBreak software provides sensitive, accurate, and comprehensive detection of structural variation in somatic next-generation sequencing data.

The Structural Variation Analysis Group of The 1000 Genomes Project reports an integrated structural variation map based on discovery and genotyping of eight major structural variation classes in 2,504 unrelated individuals from across 26 populations; structural variation is compared within and between populations and its functional impact is quantified.

Structural variants (SV) can accumulate in repeat-rich parts of the genome and transform them in unexpected ways. Here, with their new assembly-based genotyper (NAHRwhals), the authors verify multi-step SVs in 37 human loci and identify alleles at risk for copy-number variation.

A deep learning ab initio method for studying magnetic materials is developed, reducing the computational cost and opening opportunities to predict the electronic properties of magnetic superstructures, such as magnetic skyrmions.

Long-read sequencing is promising for the detection of structural variants (SVs), which requires algorithms with high sensitivity and precision. Here, the authors develop DeBreak, an algorithm for comprehensive and accurate SV detection in long-read sequencing data across different platforms, which outperforms other SV callers.

Next generation sequencing allows the identification of oncogenic driver genes in pancreatic cancer. Here, in an effort to identify additional causal genes, the authors develop a high throughput in vivoscreen and identify genes that whilst infrequently mutated in pancreatic cancer contribute to tumour formation.

KRAS-driven lung cancers represent an aggressive form of NSCLC. In this study the authors perform an in vivo functional screening and identify GATAD2B as a driver of tumor growth and metastasis in KRAS-driven lung cancer.