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.

Cyclohexene isotopologues and stereoisotopomers with varying degrees of deuteration are formed by binding a tungsten complex to benzene, which facilitates the selective incorporation of deuterium into any position on the ring.

Coherent ranging, also known as frequency-modulated continuous-wave (FMCW) laser-based light detection and ranging (lidar)(1) is used for long-range three-dimensional distance and velocimetry in autonomous driving(2,3). FMCW lidar maps distance to frequency(4,5) using frequency-chirped waveforms and simultaneously measures the Doppler shift of the reflected laser light, similar to sonar or radar(6,7) and coherent detection prevents interference from sunlight and other lidar systems. However, coherent ranging has a lower acquisition speed and requires precisely chirped(8) and highly coherent(5) laser sources, hindering widespread use of the lidar system and impeding parallelization, compared to modern time-of-flight ranging systems that use arrays of individual lasers. Here we demonstrate a massively parallel coherent lidar scheme using an ultra-low-loss photonic chip-based soliton microcomb(9). By fast chirping of the pump laser in the soliton existence range(10) of a microcomb with amplitudes of up to several gigahertz and a sweep rate of up to ten megahertz, a rapid frequency change occurs in the underlying carrier waveform of the soliton pulse stream, but the pulse-to-pulse repetition rate of the soliton pulse stream is retained. As a result, the chirp from a single narrow-linewidth pump laser is transferred to all spectral comb teeth of the soliton at once, thus enabling parallelism in the FMCW lidar. Using this approach we generate 30 distinct channels, demonstrating both parallel distance and velocity measurements at an equivalent rate of three megapixels per second, with the potential to improve sampling rates beyond 150 megapixels per second and to increase the image refresh rate of the FMCW lidar by up to two orders of magnitude without deterioration of eye safety. This approach, when combined with photonic phase arrays(11) based on nanophotonic gratings(12), provides a technological basis for compact, massively parallel and ultrahigh-frame-rate coherent lidar systems.

Epitaxial heterostructures based on oxide perovskites and III-V, II-VI and transition metal dichalcogenide semiconductors form the foundation of modern electronics and optoelectronics(1-7). Halide perovskites-an emerging family of tunable semiconductors with desirable properties-are attractive for applications such as solution-processed solar cells, light-emitting diodes, detectors and lasers(8-15). Their inherently soft crystal lattice allows greater tolerance to lattice mismatch, making them promising for heterostructure formation and semiconductor integration(16,17). Atomically sharp epitaxial interfaces are necessary to improve performance and for device miniaturization. However, epitaxial growth of atomically sharp heterostructures of halide perovskites has not yet been achieved, owing to their high intrinsic ion mobility, which leads to interdiffusion and large junction widths(18-21), and owing to their poor chemical stability, which leads to decomposition of prior layers during the fabrication of subsequent layers. Therefore, understanding the origins of this instability and identifying effective approaches to suppress ion diffusion are of great importance(22-26). Here we report an effective strategy to substantially inhibit in-plane ion diffusion in two-dimensional halide perovskites by incorporating rigid pi-conjugated organic ligands. We demonstrate highly stable and tunable lateral epitaxial heterostructures, multiheterostructures and superlattices. Near-atomically sharp interfaces and epitaxial growth are revealed by low-dose aberration-corrected high-resolution transmission electron microscopy. Molecular dynamics simulations confirm the reduced heterostructure disorder and larger vacancy formation energies of the two-dimensional perovskites in the presence of conjugated ligands. These findings provide insights into the immobilization and stabilization of halide perovskite semiconductors and demonstrate a materials platform for complex and molecularly thin superlattices, devices and integrated circuits.

An epitaxial growth strategy that improves the stability of two-dimensional halide perovskites by inhibiting ion diffusion in their heterostructures using rigid pi-conjugated ligands is demonstrated, and shows near-atomically sharp interfaces.