Spectral focusing of broadband silver electroluminescence in nanoscopic FRET-LEDs

Few inventions have shaped the world like the incandescent bulb. Edison used thermal radiation from ohmically heated conductors, but some noble metals also exhibit ‘cold’ electroluminescence in percolation films1,2, tunnel diodes3, electromigrated nanoparticle aggregates4,5, optical antennas6 or scanning tunnelling microscopy7,8,9. The origin of this radiation, which is spectrally broad and depends on applied bias, is controversial given the low radiative yields of electronic transitions. Nanoparticle electroluminescence is particularly intriguing because it involves localized surface-plasmon resonances with large dipole moments. Such plasmons enable very efficient non-radiative fluorescence resonance energy transfer (FRET) coupling to proximal resonant dipole transitions. Here, we demonstrate nanoscopic FRET–light-emitting diodes which exploit the opposite process, energy transfer from silver nanoparticles to exfoliated monolayers of transition-metal dichalcogenides10. In diffraction-limited hotspots showing pronounced photon bunching, broadband silver electroluminescence is focused into the narrow excitonic resonance of the atomically thin overlayer. Such devices may offer alternatives to conventional nano-light-emitting diodes11 in on-chip optical interconnects

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology