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.

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy(1,2). Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy(3-8). Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products(9-16), and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 +/- 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction(17). Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.

The measurement sensitivity of quantum probes using N uncorrelated particles is restricted by the standard quantum limit(1), which is proportional to 1/root N. This limit, however, can be overcome by exploiting quantum entangled states, such as spin-squeezed states(2). Here we report the measurement-based generation of a quantum state that exceeds the standard quantum limit for probing the collective spin of 10(11) rubidium atoms contained in a macroscopic vapour cell. The state is prepared and verified by sequences of stroboscopic quantum non-demolition (QND) measurements. We then apply the theory of past quantum states(3,4) to obtain spin state information from the outcomes of both earlier and later QND measurements. Rather than establishing a physically squeezed state in the laboratory, the past quantum state represents the combined system information from these prediction and retrodiction measurements. This information is equivalent to a noise reduction of 5.6 decibels and a metrologically relevant squeezing of 4.5 decibels relative to the coherent spin state. The past quantum state yields tighter constraints on the spin component than those obtained by conventional QND measurements. Our measurement uses 1,000 times more atoms than previous squeezing experiments(5-10), with a corresponding angular variance of the squeezed collective spin of 4.6 x 10(-13) radians squared. Although this work is rooted in the foundational theory of quantum measurements, it may find practical use in quantum metrology and quantum parameter estimation, as we demonstrate by applying our protocol to quantum enhanced atomic magnetometry.

Phosphorylation of INSIG1 and INSIG2 by PCK1 leads to a reduction in the binding of sterols, the activation of SREBP1 and SREBP2 and the downstream transcription of lipogenesis-associated genes that promote tumour growth.

Cancer cells increase lipogenesis for their proliferation and the activation of sterol regulatory element-binding proteins (SREBPs) has a central role in this process. SREBPs are inhibited by a complex composed of INSIG proteins, SREBP cleavage-activating protein (SCAP) and sterols in the endoplasmic reticulum. Regulation of the interaction between INSIG proteins and SCAP by sterol levels is critical for the dissociation of the SCAP-SREBP complex from the endoplasmic reticulum and the activation of SREBPs(1,2). However, whether this protein interaction is regulated by a mechanism other than the abundance of sterol-and in particular, whether oncogenic signalling has a role-is unclear. Here we show that activated AKT in human hepatocellular carcinoma (HCC) cells phosphorylates cytosolic phosphoenolpyruvate carboxykinase 1 (PCK1), the rate-limiting enzyme in gluconeogenesis, at Ser90. Phosphorylated PCK1 translocates to the endoplasmic reticulum, where it uses GTP as a phosphate donor to phosphorylate INSIG1 at Ser207 and INSIG2 at Ser151. This phosphorylation reduces the binding of sterols to INSIG1 and INSIG2 and disrupts the interaction between INSIG proteins and SCAP, leading to the translocation of the SCAP-SREBP complex to the Golgi apparatus, the activation of SREBP proteins (SREBP1 or SREBP2) and the transcription of downstream lipogenesis-related genes, proliferation of tumour cells, and tumorigenesis in mice. In addition, phosphorylation of PCK1 at Ser90, INSIG1 at Ser207 and INSIG2 at Ser151 is not only positively correlated with the nuclear accumulation of SREBP1 in samples from patients with HCC, but also associated with poor HCC prognosis. Our findings highlight the importance of the protein kinase activity of PCK1 in the activation of SREBPs, lipogenesis and the development of HCC.