资源环境科技发展态势分析平台

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.

Traditional metallic alloys are mixtures of elements in which the atoms of minority species tend to be distributed randomly if they are below their solubility limit, or to form secondary phases if they are above it. The concept of multiple-principal-element alloys has recently expanded this view, as these materials are single-phase solid solutions of generally equiatomic mixtures of metallic elements. This group of materials has received much interest owing to their enhanced mechanical properties(1-5). They are usually called medium-entropy alloys in ternary systems and high-entropy alloys in quaternary or quinary systems, alluding to their high degree of configurational entropy. However, the question has remained as to how random these solid solutions actually are, with the influence of short-range order being suggested in computational simulations but not seen experimentally(6,7). Here we report the observation, using energy-filtered transmission electron microscopy, of structural features attributable to short-range order in the CrCoNi medium-entropy alloy. Increasing amounts of such order give rise to both higher stacking-fault energy and hardness. These findings suggest that the degree of local ordering at the nanometre scale can be tailored through thermomechanical processing, providing a new avenue for tuning the mechanical properties of medium- and high-entropy alloys.

Metal alloys consisting of three or more major elemental components show enhanced mechanical properties, which are now shown to be correlated with short-range order observed with electron microscopy.

Observations show robust near-surface trends in Southern Hemisphere tropospheric circulation towards the end of the twentieth century, including a poleward shift in the mid-latitude jet(1,2), a positive trend in the Southern Annular Mode(1,3-6) and an expansion of the Hadley cell(7,8). It has been established that these trends were driven by ozone depletion in the Antarctic stratosphere due to emissions of ozone-depleting substances(9-11). Here we show that these widely reported circulation trends paused, or slightly reversed, around the year 2000. Using a pattern-based detection and attribution analysis of atmospheric zonal wind, we show that the pause in circulation trends is forced by human activities, and has not occurred owing only to internal or natural variability of the climate system. Furthermore, we demonstrate that stratospheric ozone recovery, resulting from the Montreal Protocol, is the key driver of the pause. Because pre-2000 circulation trends have affected precipitation(12-14), and potentially ocean circulation and salinity(15-17), we anticipate that a pause in these trends will have wider impacts on the Earth system. Signatures of the effects of the Montreal Protocol and the associated stratospheric ozone recovery might therefore manifest, or have already manifested, in other aspects of the Earth system.

Halide perovskite materials have promising performance characteristics for low-cost optoelectronic applications. Photovoltaic devices fabricated from perovskite absorbers have reached power conversion efficiencies above 25 per cent in single-junction devices and 28 per cent in tandem devices(1,2). This strong performance (albeit below the practical limits of about 30 per cent and 35 per cent, respectively(3)) is surprising in thin films processed from solution at low-temperature, a method that generally produces abundant crystalline defects(4). Although point defects often induce only shallow electronic states in the perovskite bandgap that do not affect performance(5), perovskite devices still have many states deep within the bandgap that trap charge carriers and cause them to recombine non-radiatively. These deep trap states thus induce local variations in photoluminescence and limit the device performance(6). The origin and distribution of these trap states are unknown, but they have been associated with light-induced halide segregation in mixed-halide perovskite compositions(7) and with local strain(8), both of which make devices less stable(9). Here we use photoemission electron microscopy to image the trap distribution in state-of-the-art halide perovskite films. Instead of a relatively uniform distribution within regions of poor photoluminescence efficiency, we observe discrete, nanoscale trap clusters. By correlating microscopy measurements with scanning electron analytical techniques, we find that these trap clusters appear at the interfaces between crystallographically and compositionally distinct entities. Finally, by generating time-resolved photoemission sequences of the photo-excited carrier trapping process(10,11), we reveal a hole-trapping character with the kinetics limited by diffusion of holes to the local trap clusters. Our approach shows that managing structure and composition on the nanoscale will be essential for optimal performance of halide perovskite devices.