资源环境科技发展态势分析平台

Microbiome signatures as putative cancer diagnostics.

Analysis of nucleic-acid sequences from human cancers, along with samples from adjacent tissue and blood, reveals the presence of microorganisms in tumours and blood across cancers.

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.

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).

The neuromodulator melatonin synchronizes circadian rhythms and related physiological functions through the actions of two G-protein-coupled receptors: MT1 and MT2. Circadian release of melatonin at night from the pineal gland activates melatonin receptors in the suprachiasmatic nucleus of the hypothalamus, synchronizing the physiology and behaviour of animals to the light-dark cycle(1-4). The two receptors are established drug targets for aligning circadian phase to this cycle in disorders of sleep(5,6) and depression(1-4,7-9). Despite their importance, few in vivo active MT1-selective ligands have been reported(2,8,10-12), hampering both the understanding of circadian biology and the development of targeted therapeutics. Here we docked more than 150 million virtual molecules to an MT1 crystal structure, prioritizing structural fit and chemical novelty. Of these compounds, 38 high-ranking molecules were synthesized and tested, revealing ligands with potencies ranging from 470 picomolar to 6 micromolar. Structure-based optimization led to two selective MT1 inverse agonists-which were topologically unrelated to previously explored chemotypes-that acted as inverse agonists in a mouse model of circadian re-entrainment. Notably, we found that these MT1-selective inverse agonists advanced the phase of the mouse circadian clock by 1.3-1.5 h when given at subjective dusk, an agonist-like effect that was eliminated in MT1- but not in MT2-knockout mice. This study illustrates the opportunities for modulating melatonin receptor biology through MT1-selective ligands and for the discovery of previously undescribed, in vivo active chemotypes from structure-based screens of diverse, ultralarge libraries. A computational screen of an ultra-large virtual library against the structure of the melatonin receptor found nanomolar ligands, and ultimately two selective MT1 inverse agonists that induced phase advancement of the mouse circadian clock when given at subjective dusk.