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Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported(1), advances in mass-spectrometry-based proteomics(2)have enabled increasingly comprehensive identification and quantification of the human proteome(3-6). However, there have been few comparisons across species(7,8), in stark contrast with genomics initiatives(9). Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides fromBacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.

Neuronal activities in the central amygdala and paraventricular nucleus are transmitted via the splenic nerve to increase plasma cell formation after immunization, and this process can be behaviourally enhanced in mice.

A hexanucleotide-repeat expansion in C9ORF72 is the most common genetic variant that contributes to amyotrophic lateral sclerosis and frontotemporal dementia(1,2). The C9ORF72 mutation acts through gain- and loss-of-function mechanisms to induce pathways that are implicated in neural degeneration(3-9). The expansion is transcribed into a long repetitive RNA, which negatively sequesters RNA-binding proteins(5) before its non-canonical translation into neural-toxic dipeptide proteins(3,4). The failure of RNA polymerase to read through the mutation also reduces the abundance of the endogenous C9ORF72 gene product, which functions in endolysosomal pathways and suppresses systemic and neural inflammation(6-9). Notably, the effects of the repeat expansion act with incomplete penetrance in families with a high prevalence of amyotrophic lateral sclerosis or frontotemporal dementia, indicating that either genetic or environmental factors modify the risk of disease for each individual. Identifying disease modifiers is of considerable translational interest, as it could suggest strategies to diminish the risk of developing amyotrophic lateral sclerosis or frontotemporal dementia, or to slow progression. Here we report that an environment with reduced abundance of immune-stimulating bacteria(10,11) protects C9orf72-mutant mice from premature mortality and significantly ameliorates their underlying systemic inflammation and autoimmunity. Consistent with C9orf72 functioning to prevent microbiota from inducing a pathological inflammatory response, we found that reducing the microbial burden in mutant mice with broad spectrum antibiotics-as well as transplanting gut microflora from a protective environment-attenuated inflammatory phenotypes, even after their onset. Our studies provide further evidence that the microbial composition of our gut has an important role in brain health and can interact in surprising ways with well-known genetic risk factors for disorders of the nervous system.

Reduced abundance of immune-stimulating gut bacteria ameliorated the inflammatory and autoimmune phenotypes of mice with mutations in C9orf72, which in the human orthologue are linked to amyotrophic lateral sclerosis and frontotemporal dementia.