资源环境科技发展态势分析平台

An efficient entanglement-based quantum key distribution is sent from the Micius satellite to two ground observatories 1,120 kilometres apart to establish secure quantum cryptography for the exchange ofquantum keys.

Quantum key distribution (QKD)(1-3)is a theoretically secure way of sharing secret keys between remote users. It has been demonstrated in a laboratory over a coiled optical fibre up to 404 kilometres long(4-7). In the field, point-to-point QKD has been achieved from a satellite to a ground station up to 1,200 kilometres away(8-10). However, real-world QKD-based cryptography targets physically separated users on the Earth, for which the maximum distance has been about 100 kilometres(11,12). The use of trusted relays can extend these distances from across a typical metropolitan area(13-16)to intercity(17)and even intercontinental distances(18). However, relays pose security risks, which can be avoided by using entanglement-based QKD, which has inherent source-independent security(19,20). Long-distance entanglement distribution can be realized using quantum repeaters(21), but the related technology is still immature for practical implementations(22). The obvious alternative for extending the range of quantum communication without compromising its security is satellite-based QKD, but so far satellite-based entanglement distribution has not been efficient(23)enough to support QKD. Here we demonstrate entanglement-based QKD between two ground stations separated by 1,120 kilometres at a finite secret-key rate of 0.12 bits per second, without the need for trusted relays. Entangled photon pairs were distributed via two bidirectional downlinks from the Micius satellite to two ground observatories in Delingha and Nanshan in China. The development of a high-efficiency telescope and follow-up optics crucially improved the link efficiency. The generated keys are secure for realistic devices, because our ground receivers were carefully designed to guarantee fair sampling and immunity to all known side channels(24,25). Our method not only increases the secure distance on the ground tenfold but also increases the practical security of QKD to an unprecedented level.

A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

Elucidating elementary mechanisms that underlie bacterial diversity is central to ecology(1,2) and microbiome research(3). Bacteria are known to coexist by metabolic specialization(4), cooperation(5) and cyclic warfare(6-8). Many species are also motile(9), which is studied in terms of mechanism(10,11), benefit(12,13), strategy(14,15), evolution(16,17) and ecology(18,19). Indeed, bacteria often compete for nutrient patches that become available periodically or by random disturbances(2,20,21). However, the role of bacterial motility in coexistence remains unexplored experimentally. Here we show that-for mixed bacterial populations that colonize nutrient patches-either population outcompetes the other when low in relative abundance. This inversion of the competitive hierarchy is caused by active segregation and spatial exclusion within the patch: a small fast-moving population can outcompete a large fast-growing population by impeding its migration into the patch, while a small fast-growing population can outcompete a large fast-moving population by expelling it from the initial contact area. The resulting spatial segregation is lost for weak growth-migration trade-offs and a lack of virgin space, but is robust to population ratio, density and chemotactic ability, and is observed in both laboratory and wild strains. These findings show that motility differences and their trade-offs with growth are sufficient to promote diversity, and suggest previously undescribed roles for motility in niche formation and collective expulsion-containment strategies beyond individual search and survival.

In mixed bacterial populations that colonize nutrient patches, a growth-migration trade-off can lead to spatial exclusion that provides an advantage to populations that become rare, thereby stabilizing the community.

River deltas rank among the most economically and ecologically valuable environments on Earth. Even in the absence of sea-level rise, deltas are increasingly vulnerable to coastal hazards as declining sediment supply and climate change alter their sediment budget, affecting delta morphology and possibly leading to erosion(1-3). However, the relationship between deltaic sediment budgets, oceanographic forces of waves and tides, and delta morphology has remained poorly quantified. Here we show how the morphology of about 11,000 coastal deltas worldwide, ranging from small bayhead deltas to mega-deltas, has been affected by river damming and deforestation. We introduce a model that shows that present-day delta morphology varies across a continuum between wave (about 80 per cent), tide (around 10 per cent) and river (about 10 per cent) dominance, but that most large deltas are tide- and river-dominated. Over the past 30 years, despite sea-level rise, deltas globally have experienced a net land gain of 54 +/- 12 square kilometres per year (2 standard deviations), with the largest 1 per cent of deltas being responsible for 30 per cent of all net land area gains. Humans are a considerable driver of these net land gains-25 per cent of delta growth can be attributed to deforestation-induced increases in fluvial sediment supply. Yet for nearly 1,000 deltas, river damming(4) has resulted in a severe (more than 50 per cent) reduction in anthropogenic sediment flux, forcing a collective loss of 12 +/- 3.5 square kilometres per year (2 standard deviations) of deltaic land. Not all deltas lose land in response to river damming: deltas transitioning towards tide dominance are currently gaining land, probably through channel infilling. With expected accelerated sea-level rise(5), however, recent land gains are unlikely to be sustained throughout the twenty-first century. Understanding the redistribution of sediments by waves and tides will be critical for successfully predicting human-driven change to deltas, both locally and globally.

A global study of river deltas shows a net increase in delta area by about 54 km(2) yr(-1) over the past 30 years, in part due to deforestation-induced sediment delivery increase.