资源环境科技发展态势分析平台

Fine-scale satellite data are used to quantify forest harvest rates in 26 European countries, finding an increase in harvested forest area of 49% and an increase in biomass loss of 69% between 2011-2015 and 2016-2018.

Clinical trials using adult stem cells to regenerate damaged heart tissue continue to this day(1,2), despite ongoing questions of efficacy and a lack of mechanistic understanding of the underlying biological effect(3). The rationale for these cell therapy trials is derived from animal studies that show a modest but reproducible improvement in cardiac function in models of cardiac ischaemic injury(4,5). Here we examine the mechanistic basis for cell therapy in mice after ischaemia-reperfusion injury, and find that-although heart function is enhanced-it is not associated with the production of new cardiomyocytes. Cell therapy improved heart function through an acute sterile immune response characterized by the temporal and regional induction of CCR2(+) and CX3CR1(+) macrophages. Intracardiac injection of two distinct types of adult stem cells, cells killed by freezing and thawing or a chemical inducer of the innate immune response all induced a similar regional accumulation of CCR2(+) and CX3CR1(+) macrophages, and provided functional rejuvenation to the heart after ischaemia-reperfusion injury. This selective macrophage response altered the activity of cardiac fibroblasts, reduced the extracellular matrix content in the border zone and enhanced the mechanical properties of the injured area. The functional benefit of cardiac cell therapy is thus due to an acute inflammatory-based wound-healing response that rejuvenates the infarcted area of the heart.

Aluminium- and gallium-doped iron compounds show a large anomalous Nernst effect owing to a topological electronic structure, and their films are potentially suitable for designing low-cost, flexible microelectronic thermoelectric generators.

Thermoelectric generation using the anomalous Nernst effect (ANE) has great potential for application in energy harvesting technology because the transverse geometry of the Nernst effect should enable efficient, large-area and flexible coverage of a heat source. For such applications to be viable, substantial improvements will be necessary not only for their performance but also for the associated material costs, safety and stability. In terms of the electronic structure, the anomalous Nernst effect (ANE) originates from the Berry curvature of the conduction electrons near the Fermi energy(1,2). To design a large Berry curvature, several approaches have been considered using nodal points and lines in momentum space(3-10). Here we perform a high-throughput computational search and find that 25 percent doping of aluminium and gallium in alpha iron, a naturally abundant and low-cost element, dramatically enhances the ANE by a factor of more than ten, reaching about 4 and 6 microvolts per kelvin at room temperature, respectively, close to the highest value reported so far. The comparison between experiment and theory indicates that the Fermi energy tuning to the nodal web-a flat band structure made of interconnected nodal lines-is the key for the strong enhancement in the transverse thermoelectric coefficient, reaching a value of about 5 amperes per kelvin per metre with a logarithmic temperature dependence. We have also succeeded in fabricating thin films that exhibit a large ANE at zero field, which could be suitable for designing low-cost, flexible microelectronic thermoelectric generators(11-13).

Environmental change is rapidly accelerating, and many species will need to adapt to survive(1). Ensuring that protected areas cover populations across a broad range of environmental conditions could safeguard the processes that lead to such adaptations(1-3). However, international conservation policies have largely neglected these considerations when setting targets for the expansion of protected areas(4). Here we show that-of 19,937 vertebrate species globally(5-8)-the representation of environmental conditions across their habitats in protected areas (hereafter, niche representation) is inadequate for 4,836 (93.1%) amphibian, 8,653 (89.5%) bird and 4,608 (90.9%) terrestrial mammal species. Expanding existing protected areas to cover these gaps would encompass 33.8% of the total land surface-exceeding the current target of 17% that has been adopted by governments. Priority locations for expanding the system of protected areas to improve niche representation occur in global biodiversity hotspots(9), including Colombia, Papua New Guinea, South Africa and southwest China, as well as across most of the major land masses of the Earth. Conversely, we also show that planning for the expansion of protected areas without explicitly considering environmental conditions would marginally reduce the land area required to 30.7%, but that this would lead to inadequate niche representation for 7,798 (39.1%) species. As the governments of the world prepare to renegotiate global conservation targets, policymakers have the opportunity to help to maintain the adaptive potential of species by considering niche representation within protected areas(1,2).

Protected areas would need to expand to 33.8% of the total land surface to adequately represent environmental conditions across the habitats of amphibians, birds and terrestrial mammals, far exceeding the current 17% target.

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.